TECHNICAL NOTE | doi:10.20944/preprints202206.0252.v1
Online: 17 June 2022 (09:00:40 CEST)
We describe an efficient and cost effective data access mechanism for Sentinel-1 TOPS 1 mode bursts. Our data access mechanism enables burst-based data access and processing, thereby 2 eliminating ESA’s Sentinel-1 SLC data packaging conventions as a bottleneck to large scale processing. 3 Pipeline throughput is now determined by available compute resources and efficiency of the analysis 4 algorithms. For targeted infrastructure monitoring studies, we are able to generate coregistered, 5 geocoded stacks of SLCs for any AOI in the world in a few minutes. In addition, we describe our 6 global scale radar backscatter and interferometric products and associated pipeline design decisions 7 that ensure geolocation consistency across the suite of derived products from Sentinel-1 data. Finally, 8 we discuss the benefits and limitations of working with geocoded SAR SLC data.
ARTICLE | doi:10.20944/preprints201910.0341.v1
Online: 29 October 2019 (15:37:11 CET)
Knowledge of the spatio-temporal occurrence of avalanche activity is critical for avalanche forecasting and hazard mapping. We present a near-real time automatic avalanche monitoring system that outputs detected avalanche polygons within roughly 10 min after Sentinel- 1 SAR data download. Our avalanche detection algorithm has an average probability of detection of 67.2 % with a false alarm rate averaging 45.9, with maximum POD's over 85 % and minimum FAR's of 24.9 % compared to manual detection of avalanches. The high variability in performance stems from the dynamic nature of snow in the Sentinel-1 data. After tuning parameters of the detection algorithm, we processed five years of Sentinel-1 images acquired over a 150 x 100 km large area in Northern Norway, with the best setup. Compared to a dataset of field-observed avalanches, 77.3 % were manually detectable. Using these manual detections as benchmark, the avalanche detection algorithm achieved an accuracy of 79 % with high POD's in cases of medium to large wet snow avalanches. For the first time, we can present a dataset of spatiotemporal avalanche activity over several winters from a large region. This unique dataset allows for research into the relationship between avalanche activity and triggering meteorological factors, mapping of avalanche prone areas and near-real time avalanche activity monitoring to assist public avalanche forecasting. Currently, the Norwegian Avalanche Warning Service is using our processing system for pre-operational use in three regions in Norway.
ARTICLE | doi:10.20944/preprints201911.0340.v1
Subject: Engineering, Electrical & Electronic Engineering Keywords: synthetic aperture radar (sar); space-borne sar; deceptive jamming
Online: 27 November 2019 (09:49:31 CET)
Due to the advantages such as low power consumption and higher concealment, deceptive jamming against synthetic aperture radar (SAR) receives extensive attention during the past decades. However, the large scene deception jamming is still a challenge because of the huge computing burden. In this paper, we propose a new large scene deceptive jamming algorithm. First, the time-delay and frequency-shift (TDFS) algorithm is introduced to improve the jamming processing speed. The system function of jammer (JSF) for a fake scatter is simplified to the multiplication of the scattering coefficient, a time-delay term in range dimension and a frequency-shift term in azimuth dimension. Then, in order to solve the problem that the effective region of the TDFS algorithm is limited, the scene deceptive jamming template is divided into several blocks according to the SAR parameters and imaging quality control factor. The JSF of each block is calculated by the TDFS algorithm and added together to achieve the large scene jamming. Finally, the correction algorithm in squint mode is derived. The simplification and parallel block processing could improve the calculation efficiency significantly. The simulation results verified the validity of the algorithm.
Online: 27 October 2020 (12:21:00 CET)
COMPASS is a mission concept aiming to determine the recent climate history of planet Mars. Aboard this mission, there would be a dual mode radar instrument capable of acting both as a Synthetic Aperture Radar (SAR) and a Subsurface Sounder for mapping shallow ice. The COMPASS Radar Observer for Mars Exploration (CROME) instrument will help advance science and exploration. First, the COMPASS mission is described. Then, the CROME instrument concept and its implementation are presented. Finally, the procedure used to predict the performance is detailed for both the Side-Looking SAR and the Nadir-looking Sounder modes.
REVIEW | doi:10.20944/preprints202208.0278.v1
Subject: Medicine & Pharmacology, Other Keywords: central stress response system; sympathetic activity; HPA axis; SAR-CoV-2; catecholamine; corti-costeriods; clonidine; dexamethasone
Online: 16 August 2022 (05:07:18 CEST)
We are in amidst of COVID-19 pandemic. Since Dec 2019, severe acute respiratory corona virus (SAR-CoV-2) has infected more than half a billion people killing nearly 7 million people worldwide. Now the BA.5 variant of SARS-CoV-2 is causing mayhem and driving the global surge. Epidemiologist are aware of the fact that this virus is capable of escaping immunity and likely to infect the same person multiple times despite adequate vaccination status. Elderly people of age more than 60 years and those with underlying health conditions are considered as high-risk who are likely to suffer complications and death. While it is tempting to frame complications and mortality from COVID-19 as a simple matter of too much of a virulent virus in too weak of a host, much more is at play here. Framing the pathophysiology of COVID-19 in the context of the Chrousos and Gold model of the central stress response system can shed insight into its complex pathogenesis. Understanding the mechanisms by which pharmacologic modulation of the central stress response system via administration of clonidine and/or dexamethasone may offer an explanation as to why a viral pathogen can be well tolerated and cleared by one host while inflaming and killing another.
ARTICLE | doi:10.20944/preprints202201.0149.v3
Subject: Life Sciences, Cell & Developmental Biology Keywords: Mussel; vitality; motility; millimeter waves; SAR
Online: 28 February 2022 (09:55:17 CET)
Recently, a rising use of wireless internet technologies has been demonstrated. The devices 18 which use these technologies emit in new spectral regions an electromagnetic radiation (EMFs) 19 which could interact with the male reproductive system. The aim of this study was to investigate in 20 vitro influence of electromagnetic fields at 27 GHz on sperm quality in Mytilus galloprovincialis. 21 Sperm samples, were collected from sexually mature males of M. galloprovincialis and placed in sea- 22 water. Once evaluated the number and quality of spermatozoa, sperm cells were exposed to elec- 23 tromagnetic fields radiated by a pyramidal horn antenna. The effect of exposure was evaluated after 24 10, 20, 30, 40 and 60 minutes with light microscope and using Eosin test. Ten replications were per- 25 formed for each time series, and statistical analysis was carried out byt-test. A significative decrease 26 in sperm motility was observed after 10 minutes of exposure and after 30 minutes most of sperms 27 were immobile and not vital. This study provides useful data on potential ecological impact of the 28 5G high-band on animal fertility, whose effect is currently under investigation.
ARTICLE | doi:10.20944/preprints202112.0280.v1
Online: 16 December 2021 (15:57:35 CET)
Synthetic Aperture Radar (SAR) is an active type of microwave remote sensing. Using the microwave imaging system, remote sensing monitoring of the land and global ocean can be done in any weather conditions around the clock. Detection of SAR image targets is one of the main needs of radar image interpretation applications. In this paper, an improved two-parameter CFAR algorithm based on Rayleigh distribution and morphological processing is proposed to perform ship detection and recognition in high resolution SAR images. Through simulation experiments, comprehensive study of the two algorithms for high resolution SAR image target detection is achieved. Finally, the results of ship detection experiments are compared and analyzed, and the effects of detection are evaluated according to the Rayleigh distribution model and algorithms.
ARTICLE | doi:10.20944/preprints201711.0112.v1
Online: 16 November 2017 (19:04:21 CET)
The Volturno Plain is one of the largest alluvial plain of peninsular Italy. This area is characterized by both natural and human induced subsidence, and is and most susceptible to coastal hazards. The present study is based on post-processing, analysis and mapping of the available Persistent Scatterer interferometry datasets, derived from combination of both ascending and descending orbits of three different SAR satellite systems, during an observation period of almost two decades (June 1992 - September 2010). The main output of the research work is a map of the vertical deformation that provides new insights into the areal variability of ground deformation processes (subsidence/uplift) of Volturno plain over the last decades. Vertical displacement values derived by interferometric data post-processing show that the Volturno river plain is characterized by significant subsidence in the central axial sectors and in the river mouth area, whereas moderate uplift is detected in the eastern part of the plain. Other sectors of the study area are characterized by moderate subsidence and/or stability. We infer that the subsidence recorded in the Volturno plain is mainly a consequence of a natural process related to the compaction of the fluvial deposits that fill up the alluvial plain. Anthropic influence (e.g. water exploitation, urbanization) can be substantially regarded as an additional factor that only locally may enhance subsidence. The uplift imaged in the eastern sector of the plain can be related to tectonic activity. The study of subsidence in the Volturno plain is a valuable tool relevant for river flood analyses and coastal inundation hazard assessment addressed to risk mitigation.
ARTICLE | doi:10.20944/preprints202301.0401.v1
Subject: Biology, Agricultural Sciences & Agronomy Keywords: SAR; Sentinel-1A; DSSAT CROPGRO; Peanut; Yield gap
Online: 23 January 2023 (08:15:49 CET)
Crop yield data is critical for managing sustainable agriculture and assessing national food security. Current study aims to increase Peanut productivity from current levels by analyzing the yield gap of production potential between theoretical yield and actual farmers’ yields. The spatial yield gap of Peanut for Thiruvannamalai district of Tamil Nadu is examined in this paper by integrating the products of microwave remote sensing (SAR Sentinel-1A) with DSSAT CROPGRO peanut simulation model. CROPGRO Peanut model was calibrated and validated by conducting field experiment at Oilseeds Research Station, Tindivanam during Rabi 2019 for predominant cultivars viz. TMV 7, TMV 13, VRI 2 and G 7. Actual attainable yield was recorded by organizing CCE with help of Department of Agriculture Economics and Statistics in the respective monitoring Villages. Regression analysis between maximum recorded DSSAT Leaf Area Index (LAI) at peak flowering stage of peanut and yield recorded by Crop Cutting Experiment (CCE) for spatial yield estimation of Peanut in Thiruvannamalai district of Tamil Nadu during Rabi 2021 was carried out using ArcGIS 10.6 software. The results showed that the simulated potential yield ranged from 3194 to 4843 kg/ha, whereas actual yield ranged from 1228 to 3106 kg/ha, with a considerable disparity between the actual and potential yield levels (1217 to 2346 kg/ha) of the monitored locations. The minimum, maximum and average yield gaps in Peanut for Thiruvannamalai district was assessed as 1890, 2324 and 2134 kg/ha, respectively. To reduce the production difference (Yield gap) of Peanut cultivation, farmers should focus more on management issues such as time of sowing, irrigation or water management, quantity and sources of nutrients, cultivar selection and availability of quality seeds tailored to each region.
COMMUNICATION | doi:10.20944/preprints202207.0450.v1
Subject: Earth Sciences, Oceanography Keywords: SAR image; ship wake; deep learning; synthetic dataset
Online: 29 July 2022 (05:51:03 CEST)
The classification of vessel types in SAR imagery is of crucial importance for maritime applications. However, the ability to use real SAR imagery for deep learning classification is limited, due to the general lack of such data and/or the labor-intensive nature of labeling them. Simulating SAR images can overcome these limitations, allowing the generation of an infinite number of datasets. In this contribution, we present a synthetic SAR imagery dataset with ship wakes, which comprises 46080 images for ten different real vessel models. The variety of simulation parameters includes 16 ship heading directions, 6 ship velocities, 8 wind directions, 2 wind velocities, and 3 incidence angles. In addition, we extensively investigate classification performance for noise-free, noisy, and denoised ship wake scenes. We utilize the standard AlexNet architecture and employ training from scratch. To achieve the best classification performance, we conduct Bayesian optimization to determine hyperparameters. Results demonstrate that the classification of vessel types based on their SAR signatures is highly efficient, with maximum accuracies of 96.16%, 92.7%, and 93.59%, when training using noise-free, noisy, and denoised datasets respectively. Thus, we conclude that the best strategy in practical applications should be to train convolutional neural networks on denoised SAR datasets. The results show that the versatility of the SAR simulator can open up new horizons in the application of machine learning to a variety of SAR platforms.
ARTICLE | doi:10.20944/preprints202201.0136.v1
Subject: Life Sciences, Cell & Developmental Biology Keywords: millimeter waves; zebrafish; DanioScope; biomarkers of exposure; SAR.
Online: 11 January 2022 (12:22:42 CET)
5G technology is evolving to satisfy several service requirements favoring high data-rate connections and lower latency times than current ones (< 1ms). 5G systems use different frequency bands of the radio wave spectrum, taking advantage of higher frequencies than previous mobile radio generations. In order to guarantee a capillary coverage of the territory for high reliability applications, it will be necessary to install a large number of repeaters because higher frequencies waves have a lower capacity to propagate in free space. Following the introduction of this new technology, there has been a growing concern about possible harmful effects on human health. The aim of this study is investigating possible short term effects induced by 5G-millimeter waves on embryonic development of Danio rerio. We have exposed fertilized eggs to 27 GHz frequency, 9.7 mW/cm2 incident power density, 23 dbm and have measured several endpoints every 24 hours. The exposure to electromagnetic fields at 27 GHz (5G) caused no significant impacts on mortality nor on morphology because the exposed larvae showed a normal detachment of the tail, presence of heart-beat and well-organised somites. A weak positivity on exposed larvae has been highlighted by immunohistochemical analysis.
ARTICLE | doi:10.20944/preprints202109.0152.v1
Subject: Earth Sciences, Environmental Sciences Keywords: SAR; Sentinel-1; Amplitude; Beach environment; Weather conditions
Online: 8 September 2021 (13:11:46 CEST)
Environmental effects and climate change are lately representing an increasing strain of the coastal areas which topography strongly depends on these conditions. However, the processes by which weather and environmental phenomena influence the highly variable beach morphology are still unknown. A continuous monitoring of the beach environment is necessary to implement protection strategies. This paper presents the results of an innovative study performed on a coastal area using satellite remote sensing data with the aim of understanding how environmental phenomena affect beaches. Two-years of synthetic aperture radar (SAR) Sentinel-1 images are used over a test area in Noordwijk, the Netherlands. At the same time as the SAR acquisitions, information on tidal and weather conditions are collected and integrated from nearby meteorological stations. Dedicated codes are implemented in order to understand the relationship between the SAR amplitude and the considered phenomena: wind, precipitation, tidal conditions. Surface roughness is taken into account. The results indicate a strong correlation between the amplitude and the wind. No particular correlation or trend could be noticed in the relation with the precipitation. The analysis of the amplitude also shows a decreasing trend moving from the dry area of the beach towards the sea and the correlation coefficient between the amplitude and the tide level gets negative with the increase of the water content.
ARTICLE | doi:10.20944/preprints202010.0444.v1
Subject: Engineering, Automotive Engineering Keywords: staggered SAR; low oversampling; compressed sensing (CS); NUFFT
Online: 21 October 2020 (16:41:58 CEST)
This paper focuses on processing low oversampling echo data of staggered synthetic aperture radar (SAR). In staggered mode, the non-uniformly sampling and irregular loss of echo data cause azimuth ambiguity which severely degrades the imaging quality. To solve this problem, we propose a compressed sensing (CS) method in which the non-uniform fast Fourier transform (NUFFT) technique is adopted to obtain uniform azimuth spectrum, and the fast iterative shrinkage thresholding algorithm (FISTA) is utilized to efficiently reconstruct the ambiguity-free image from in-complete echo data. Simulation results demonstrate the proposed method can effectively suppress the azimuth ambiguity in the vicinity of targets.
ARTICLE | doi:10.20944/preprints202010.0209.v1
Subject: Engineering, Automotive Engineering Keywords: SAR amplitude; Laplace distribution; Rician distribution; Statistical modelling
Online: 9 October 2020 (15:47:49 CEST)
This paper presents a novel statistical model i.e. the Laplace-Rician distribution, for the characterisation of synthetic aperture radar (SAR) images. Since accurate statistical models lead to better results in applications such as target tracking, classification, or despeckling, characterising SAR images of various scenes including urban, sea surface, or agricultural, is essential. The proposed Laplace-Rician model is investigated for SAR images of several frequency bands and various scenes in comparison to state-of-the-art statistical models that include K, Weibull, and Lognormal. The results demonstrate the superior performance and flexibility of the proposed model for all frequency bands and scenes.
Subject: Earth Sciences, Oceanography Keywords: synthetic aperture radar (SAR); wave mode; ocean waves
Online: 15 May 2020 (18:22:41 CEST)
This dataset consists of integral sea state parameters of significant wave height (SWH) and mean wave period (zero-upcrossing mean wave period, MWP) data derived from the advanced synthetic aperture radar (ASAR) onboard the ENVISAT satellite over its full life cycle (2002-2012) covering the global ocean. Both parameters are calibrated and validated against buoy data. A cross-validation between the ASAR SWH and radar altimeter (RA) data is also performed to ensure that the SAR-derived wave height data are of the same quality as the RA data. These data are stored in the standard NetCDF format, which are produced for each ASAR wave mode Level1B data provided by the European Space Agency. This is the first time that a full sea state product in terms of both the SWH and MWP has been derived from spaceborne SAR data over the global ocean for a decadal temporal scale.
ARTICLE | doi:10.20944/preprints201811.0424.v1
Subject: Earth Sciences, Other Keywords: altimetry; retracking; Sentinel-3; synthetic aperture radar (SAR)
Online: 19 November 2018 (06:55:41 CET)
Satellite altimeters have been used to monitor river and reservoir water levels, from which water storage estimates can be derived. Inland water altimetry can therefore play an important role in continental water resource management. Traditionally, satellite altimeters were designed to monitor homogeneous surfaces such as oceans or ice sheets, resulting in a poor performance over small inland water bodies due to the contribution from land contamination in the returned waveforms. The advent of synthetic aperture radar (SAR) altimetry (with its improved along-track spatial resolution) has enabled the measurement of inland water levels with a better accuracy and an increased spatial resolution. This paper presents three specialized algorithms or retrackers to retrieve water levels from SAR altimeter data over inland water bodies dedicated to minimizing land contamination from the waveforms. The performances of the proposed waveform portion selection method with three retrackers, namely, the threshold retracker, Offset Centre of Gravity (OCOG) retracker and 2-step physical-based retracker, are compared. Time series of water levels are retrieved for water bodies in the Ebro River basin (Spain). The results show good agreement with in situ measurements from the Ebro Reservoir (width is approximately 1.8 km) and Ribarroja Reservoir (width is approximately 400 m) with un-biased root-mean-square errors (RMSEs) of approximately 0.28 m and 0.16 m, respectively. The performances of all three retrackers are also compared with the European Space Agency’s ocean retracker in the Sentinel-3 Level-2 product.
ARTICLE | doi:10.20944/preprints202201.0417.v1
Subject: Earth Sciences, Geoinformatics Keywords: Deformation Monitoring; Land Subsidence; Coastal Areas; PSI; SAR; Cyprus
Online: 27 January 2022 (11:30:23 CET)
Abstract: In the last five years, the urban development of Limassol City has rapidly increased in the sectors of industry, trade, real estate, and many others. This exponentially increased urban development introduces several concerns about the aggravation of the land subsidence in the Limassol coastal front. Fifty Copernicus Sentinel-1 data from 2017-2021 have been processed and analyzed using the Sentinel Application Platform (SNAP) and the Stanford Method for Persistent Scatters (StaMPS). A case study for the identification and analysis of the elements (PS) in pixels in a series of interferograms, and then, the quantity of the land displacements in the Line of Sight, in the Limassol coastal front, is presented in this research, with the subsidence rates up to about (-5 to 4 mm / year). For the validation of the detected deformation, accurate ground-based geodetic measurements along the coastal area were used. Concordantly, taking into account that there are a significant number of skyscrapers planned to be built, this study attempts a preliminary assessment of the impact these structures will pose on the coastal front of the area of Limassol.
ARTICLE | doi:10.20944/preprints202104.0719.v1
Subject: Engineering, Automotive Engineering Keywords: auotofocus; minimum entropy; multi-subaperture; synthetic aperture radar (SAR)
Online: 27 April 2021 (12:50:34 CEST)
Autofocus is an essential part of the SAR imaging process. Multi-subaperture autofocus algorithm is a commonly used autofocus algorithm for processing SAR stripmap mode data. The multi-subaperture autofocus algorithm has two main steps, the first is to estimate the phase error gradient within the subaperture, the second is to splice the phase error gradient, that is, to remove the shift amount between the estimated adjacent subapertures’ error gradients. Previous gradient-splicing algorithms assume that the estimation of subaperture error is accurate, but when the estimation of subaperture phase error gradients is not accurate enough, these algorithm performance will be degraded. A new phase error gradient splicing algorithm is proposed in this paper. It roughly estimates the shift amount first, and then finely estimates the shift amount based on the minimum-entropy criterion, which can improve the robustness of splicing especially when the estimation of the phase error gradients of the subaperture is not accurate enough. To speed up the algorithm, a variable-step-size search method is used. Simulation and experimental results show that the algorithm has enough accuracy and still has good performance when other splicing algorithms doesn’t perform well.
ARTICLE | doi:10.20944/preprints202102.0083.v1
Subject: Mathematics & Computer Science, Algebra & Number Theory Keywords: SAR image classification; Spiking Neural Network(SNN); unsupervised learning
Online: 2 February 2021 (10:35:38 CET)
Recent neuroscience research results show that the nerve information in the brain is not only encoded by the spatial information. Spiking neural network based on pulse frequency coding plays a very important role in dealing with the problem of brain signal, especially complicated space-time information. In this paper, an unsupervised learning algorithm for bilayer feedforward spiking neural networks based on spike-timing dependent plasticity (STDP) competitiveness is proposed and applied to SAR image classification on MSTAR for the first time. The SNN learns autonomously from the input value without any labeled signal and the overall classification accuracy of SAR targets reached 80.8%. The experimental results show that the algorithm adopts the synaptic neurons and network structure with stronger biological rationality, and has the ability to classify targets on SAR image. Meanwhile, the feature map extraction ability of neurons is visualized by the generative property of SNN, which is a beneficial attempt to apply the brain-like neural network into SAR image interpretation.
ARTICLE | doi:10.20944/preprints201909.0130.v1
Subject: Chemistry, Medicinal Chemistry Keywords: Synthesis; triazinoindole; thiosemicarbazide; alpha-glucosidase; molecular docking study; SAR
Online: 13 September 2019 (10:54:30 CEST)
New class of triazinoindole bearing thiosemicarbazide (1-25) was synthesized and evaluated for α-glucosidase inhibitory potential. All synthesized analogues exhibited excellent inhibitory potential having IC50 values ranging from 1.30 ± 0.01 to 35.80 ± 0.80 µM when compared with the standard acarbose having IC50 value 38.60 ± 0.20 µM. Among series the analogues 1 and 23 was found the most potent having IC50 values 1.30 ± 0.05 and 1.30 ± 0.01 µM respectively. Structure activity relationship (SAR) was mainly based upon by bring about difference of substituents on phenyl rings. To confirm the binding interactions, molecular docking study was performed. Synthesized analogues were characterized through HREI-MS, 1H and 13C-NMR analysis.
ARTICLE | doi:10.20944/preprints201804.0251.v1
Subject: Mathematics & Computer Science, Information Technology & Data Management Keywords: ATR; ISAR/SAR images; saliency attention; SIFT; multitask-SRC
Online: 19 April 2018 (10:32:02 CEST)
In this paper, we propose a novel approach to recognize radar targets on inverse synthetic aperture radar (ISAR) and synthetic aperture radar (SAR) images. This approach is based on the multiple salient keypoint descriptors (MSKD) and multitask sparse representation based classification (MSRC). Thus, to characterize the targets in the radar images, we combine the scale-invariant feature transform (SIFT) and the saliency map. The goal of this combination is to reduce the SIFT keypoints and their time computing time by maintaining only those located in the target area (salient region). Then, we compute the feature vectors of the resulting salient SIFT keypoints (MSKD). This methodology is applied for both training and test images. The MSKD of the training images leads to construct the dictionary of a sparse convex optimization problem. To achieve the recognition, we adopt the MSRC taking into consideration each vector in the MSKD as a task. This classifier solves the sparse representation problem for each task over the dictionary and determines the class of the radar image according to all sparse reconstruction errors (residuals). The effectiveness of the proposed approach method has been demonstrated by a set of extensive empirical results on ISAR and SAR images databases. The results show the ability of our method to predict adequately the aircraft and the ground targets.
ARTICLE | doi:10.20944/preprints202212.0136.v1
Subject: Arts & Humanities, Art History & Restoration Keywords: SAR; Climate Change; GIS; Archaeological Chart; Risk Assessment; Digital Humanities.
Online: 8 December 2022 (01:12:38 CET)
The province of Jaén (Andalusia, Spain), despite being declared the European territory with the largest number of defensive constructions (castles and fortifications), has few conservation plans, with many remains included on the Red List of Spanish Heritage lying abandoned. This presents a problem for the conservation of the landscape and the optimal use of the province’s tourism potential. Two actions are proposed to alleviate this situation: The creation of an archaeological and environmental risk chart with which to answer such ques-tions as “How have climate change, anthropic alterations and environmental characteristics af-fected the state of conservation of certain heritage sites?” and to put forward proposals for im-proving their protection using as a basis digital and technological tools, such as remote sensing (SAR), taking advantage of data from the Sentinel 2A and 2B satellites, HBIM, RPAS and GIS. To foster the promotion of smart tourism by digitalising and virtualising tourist routes and ar-chaeological remains by building a Smart Tourism App for mobile devices. Finally, public administrations will be apprised of the need to implement a conservation policy for cultural assets and their surroundings in a simple, quick and cost-effective manner.
ARTICLE | doi:10.20944/preprints202201.0434.v1
Subject: Earth Sciences, Environmental Sciences Keywords: Terrestrial Laser Scanner; SAR; coastal environment; weather effect; surface roughness.
Online: 28 January 2022 (11:18:54 CET)
In the past years, our knowledge of coastal environments has been enriched by remotely sensed data. However, to successfully extract information from a combination of different sensors systems, it should be understood how these interact with the common coastal environment. In this research we co-analyze two sensor systems: Terrestrial Laser Scanning (TLS) and satellite based Synthetic Aperture Radar (SAR). TLS shows large potential for examining coastal processes thanks to the possibility to retrieve repeated, accurate and dense topographic information in a rapid and non-invasive manner. However, TLS presents some limits due to its high economic costs and limited field of view. SAR systems are among the most used active remote sensor system for Earth Observation. Despite their relatively low resolution, SAR systems provide the ability to monitor and map coastal areas with complete, repeated and frequent coverage, penetrating through clouds and providing all weather monitoring. Moreover, Sentinel-1 SAR images are freely available. The availability of a permanently installed TLS system (PLS, Permanent Laser Scanner) allows us, to extensively compare Sentinel-1 SAR data and topographic laser scans during different conditions on a sandy beach. PLS data are compared with simultaneous Sentinel-1 SAR images in order to investigate the combined use of PLS and SAR in coastal environments. The purpose of this comparison is the investigation of a possible relation between PLS and SAR data: knowing their relation, SAR dataset could be correlated to beaches characteristics. Meteorological and surface roughness have also been taken into consideration in the evaluation of the correlation between PLS and SAR data. The permanently installed laser scanner for the present study is located in Noordwijk (the Netherlands). A generally positive but low correlation exists between the two variables. When considering weather phenomena, their correlation increases and shows a dependence on wind directions and speed. The correlation with the surface roughness, evaluated in terms of root-mean squared height, also depends on specific wind speed and directions.
Subject: Earth Sciences, Geology Keywords: SAR Interferometry; Sentinel-1; deformation monitoring; tectonics; volcanism; automatic processing
Online: 3 June 2020 (04:51:11 CEST)
Space-borne Synthetic Aperture Radar (SAR) Interferometry (InSAR) is now a key geophysical tool for surface deformation studies. The European Commission’s Sentinel-1 Constellation began acquiring data systematically in late 2014. The data, which are free and open access, have global coverage at moderate resolution with a 6 or 12-day revisit, enabling researchers to investigate large-scale surface deformation systematically through time. However, full exploitation of the potential of Sentinel-1 requires specific processing approaches as well as the efficient use of modern computing and data storage facilities. Here we present LiCSAR, an operational system built for large-scale interferometric processing of Sentinel-1 data. LiCSAR is designed to automatically produce geocoded wrapped and unwrapped interferograms and coherence estimates, for large regions, at 0.001° resolution (WGS-84 system). The products are continuously updated in a frequency depending on prioritised regions (monthly, weekly or live update strategy). The products are open and freely accessible and downloadable through an online portal. We describe the algorithms, processing, and storage solutions implemented in LiCSAR, and show several case studies that use LiCSAR products to measure tectonic and volcanic deformation. We aim to accelerate the uptake of InSAR data by researchers as well as non-expert users by mass producing interferograms and derived products.
ARTICLE | doi:10.20944/preprints201907.0191.v1
Subject: Earth Sciences, Environmental Sciences Keywords: forest types; forest mapping; Sentinel-2; SAR; LiDAR; canopy metrics
Online: 16 July 2019 (08:12:02 CEST)
Indigenous forests cover 24% of New Zealand and provide valuable ecosystem services. However, a national map of forest types, that is, physiognomic types, which would benefit conservation management, does not currently exist at an appropriate level of detail. While traditional forest classification approaches from remote sensing data are based on spectral information alone, the joint use of space-based optical imagery and structural information from synthetic aperture radar (SAR) and canopy metrics from air-borne Light Detection and Ranging (LiDAR) facilitates more detailed and accurate classifications of forest structure. We present a support vector machine (SVM) classification using data from ESA’s Sentinel-1 and 2 missions, ALOS PALSAR, and airborne LiDAR to produce a regional map of physiognomic types of indigenous forest in New Zealand. A five-fold cross-validation of ground data showed that the highest classification accuracy of 80.9% is achieved for bands 2, 3, 4, 5, 8, 11, and 12 from Sentinel-2, the ratio of bands VH and VV from Sentinel-1, HH from PALSAR, and mean canopy height and 97th percentile canopy height from LiDAR. The classification based on the optical bands alone was 73.1% accurate and the addition of structural metrics from SAR and LiDAR increased accuracy by 7.8%. The classification accuracy is sufficient for many management applications for indigenous forest in New Zealand, including biodiversity management, carbon inventory, pest control, ungulate management, and disease management. National application of the method will be possible in several years, once national LiDAR coverage is achieved, and a national canopy height model is available.
ARTICLE | doi:10.20944/preprints201902.0185.v1
Subject: Earth Sciences, Oceanography Keywords: C-band SAR; sea surface wind speed retrieval; full polarimetry
Online: 20 February 2019 (09:07:35 CET)
In this paper, sea surface wind speed (SSWS) retrieval from Gaofen-3 (GF-3) quad-polarization stripmap (QPS) data in vertical-vertical (VV), horizontal-horizontal (HH) and vertical-horizontal (VH) polarizations is investigated in detail based on 3,170 scenes acquired from October 2016 to May 2018. The radiometric calibration factor of the VV polarization data is examined first. This calibration factor generally meets the requirement of SSWS retrieval accuracy with an absolute bias of less than 0.5 m/s but shows highly dispersed characteristics. These results lead to SSWS retrievals with a small bias of 0.18 m/s but a rather high root mean square error (RMSE) of 2.36 m/s compared with the ERA-Interim reanalysis model data. Two refitted polarization ratio (PR) models for the QPS HH polarization data are presented. Based on a combination of the incidence angle- and azimuth angle-dependent PR model and CMOD5.N, the SSWS derived from the QPS HH data shows a bias of 0.07 m/s and an RMSE of 2.26 m/s relative to the ERA-Interim reanalysis model wind speed. A linear function relating SSWS and the normalized radar cross section (NRCS) of QPS VH data is derived. The SSWS data retrieved from the QPS VH data show good agreement with the WindSat SSWS data, with a bias of 0.1 m/s and an RMSE of 2.02 m/s. We also apply the linear function to the GF-3 Wide ScanSAR data acquired for the typhoon SOULIK, which surprisingly yields a very good agreement with the model results. A comparison of SSWS retrievals among three different polarization datasets is also presented. The current study and our previous work demonstrate that the general accuracy of the SSWS retrieval based on GF-3 QPS data has an absolute bias of less than 0.3 m/s and an RMSE of 2.0 ±0.2 m/s relative to various datasets. Further improvement will depend on dedicated radiometric calibration efforts.
ARTICLE | doi:10.20944/preprints201808.0504.v1
Subject: Earth Sciences, Oceanography Keywords: ship detection; hyperspectral; SAR; optical remote sensing; sustainability; coastal region
Online: 29 August 2018 (14:32:09 CEST)
As human activities of the countries in the East Asia have been remarkably expanding over recent decades, various problems in relation to ships, such as oil spill and many other coastal marine pollution, are continuously occurring in the coastal region. In order to conserve marine resources and prepare for possible ship accidents in advance, the need for efficient ship management is increasing over time. Multi-satellite, multi-sensor, multi-wavelength or multi-frequency observations make it possible to monitor a variety of vessels in the coastal region. This study presents the results of ship detection methodology applied to multi-spectral satellite images in the seas around Korean Peninsula based on optical, hyperspectral, and microwave remote sensing. To detect ships from hyperspectral images with a few hundreds of spectral channels, spectral matching algorithms are used to investigate similarity between the spectra and in-situ measurements. In the case of SAR (Synthetic Aperture Radar) images, the Constant False Alarm Rate (CFAR) algorithm is used to discriminate the vessels from backscattering coefficients of Sentinel-1 SAR and ALOS-2 PALSAR2 images. The present ship detection methods can be extensively utilized for optical, hyperspectral, and SAR images for comprehensive coastal management purposes toward perpetual sustainability in the future.
ARTICLE | doi:10.20944/preprints201807.0383.v1
Subject: Physical Sciences, Applied Physics Keywords: SAR speckle; rough surface scattering; exponential correlation; very high resolution
Online: 20 July 2018 (12:52:02 CEST)
The aim of this study is to investigate, by means of experimental measurements and full-wave simulations, the dominant factors for the very high-resolution (VHR) radar image speckles from exponential correlated rough surfaces. A Ka-band radar system was used to collect the return signal from such a surface sample fabricated by 3D printing, and that signal was further processed into images at different resolution scales, where the image samples were obtained by horizontally turning around the surface sample. To cross-validate the results and to further discuss the VHR speckle properties, full wave simulations by full 3D Finite Difference Time Domain (FDTD) method were conducted with 1600 realizations for the speckle analysis. At the considered very high resolution, speckle statistics show divergence from the fully developed Rayleigh distribution. The factors that impact on the high-resolution speckle properties from exponential correlated rough surface, are analyzed in views of the equivalent number of scatterers theory and scattering scales, respectively. From the data results and extended discussions, it is evident that both of the above factors matter for VHR speckle of backscattering, from the exponential correlated rough surface as a good representative for the ground surface.
ARTICLE | doi:10.20944/preprints201806.0393.v1
Subject: Engineering, Electrical & Electronic Engineering Keywords: reconfigurable architecture; CORDIC; Field Programmable Gate Array(FPGA); SAR imaging
Online: 25 June 2018 (14:42:01 CEST)
This paper presents a unified reconfigurable coordinate rotation digital computer (CORDIC) processor for floating-point arithmetic. It can be configured to operate in multi-mode to achieve a variety of operations and replaces multiple single-mode CORDIC processors. A reconfigurable pipeline-parallel mixed architecture is proposed to adapt different operations, which maximizes the sharing of common hardware circuit and achieves the area-delay-efficiency. Compared with previous unified floating-point CORDIC processors, the consumption of hardware resources is greatly reduced. As a proof of concept, we apply it to 1638416384 points target Synthetic Aperture Radar (SAR) imaging system, which is implemented on Xilinx XC7VX690T FPGA platform. The maximum relative error of each phase function between hardware and software computation and the corresponding SAR imaging result can meet the accuracy index requirements.
ARTICLE | doi:10.20944/preprints201608.0055.v1
Subject: Earth Sciences, Other Keywords: seismic damage building; watershed segmentation; SAR; texture feature; change detection
Online: 5 August 2016 (12:19:24 CEST)
The information of seismic damage of buildings in SAR images of different time phase, especially in SAR images after earthquake, is easily disturbed by other factors, which affects the accuracy of information discrimination. In order to identify and evaluate the distribution information of the seismic damage accurately and make full use of the abundant texture features in the SAR image. The conventional method of change detection based on texture features usually takes the pixel as the calculating unit. In this paper, a method of texture feature change detection of SAR images based on watershed segmentation algorithm is proposed. Based on the optimization of texture feature parameters, the feature parameters are segmented by the watershed segmentation algorithm, and the feature object image is obtained. This method introduces the idea of object oriented, and carries out the calculation of the difference map at the object level, Finally, the classification threshold value of different types of seismic damage types is selected, and the recognition of building damage is achieved. Taking the ALOS data before and after the earthquake in Yushu as an example to verify the effectiveness of the method, the overall accuracy of the building extraction is 88.9%, Compared with pixel-based methods, it is proved that the proposed method is effective.
ARTICLE | doi:10.20944/preprints202109.0188.v1
Subject: Chemistry, Medicinal Chemistry Keywords: pyrazolo-pyrido-pyrimidines; cytotoxicity; tumor cell lines; SAR; in silico docking.
Online: 10 September 2021 (15:07:59 CEST)
To explore a new set of anticancer agents, a novel series of pyrazolo[4,3-e]pyrido[1,2-a]pyrimidine derivatives 7a-l have been designed and synthesized via cyclocondensation reactions of pyrazolo-enaminone 5 with a series of arylidene malononitriles; compound 5 was obtained from 5-amino-4-cyanopyrazole (3). The structures of the target compounds 7a-l were investigated by spectral techniques and elemental analysis (IR, UV-Vis, 1H NMR, 13C NMR and ESI-MS). All compounds were evaluated for their in vitro cytotoxicity employing a panel of different human tumor cell lines, A375, HT29, MCF7, A2780, FaDu as well as non-malignant NIH 3T3 and HEK293 cells. It has been found that the conjugate 7e was the most active towards many cell lines with EC50 values ranging between 9.1 and 13.5 µM, respectively. Moreover, in silico docking studies of 7e with six anticancer drug targets, i.e. DHFR, VEGFR2, HER-2/neu, hCA-IX, CDK6 and LOX also was performed, in order to gain some insights into their putative mode of binding interaction and to estimate the free binding energy of this bioactive molecule.
ARTICLE | doi:10.20944/preprints201807.0624.v1
Subject: Earth Sciences, Environmental Sciences Keywords: flood mapping; Multispectral; SAR; free satellite data; Ebro basin; Po basin
Online: 31 July 2018 (12:45:03 CEST)
Satellite remote sensing is a powerful tool to map flooded areas. In the last years, the availability of free satellite data sensibly increased in terms of type and frequency, allowing producing flood maps at low cost around the World. In this work, we propose a semi-automatic method for flood mapping, based only on free satellite images and open-source software. As case studies, we selected three flood events recently occurred in Spain and Italy. Multispectral satellite data acquired by MODIS, Proba-V, Landsat, Sentinel-2 and SAR data collected by Sentinel-1 were used to detect flooded areas using different methodologies (e.g., MNDWI; SAR backscattering variation; Supervised classification). Then, we improved and manually refined the automatic mapping using free ancillary data like DEM based water depth model and available ground truth data. For the areas affected by major floods, we also validated and compared the produced flood maps with official maps made by river authorities. We calculated flood detection performance (flood ratio) for the different datasets we used. The results show that it is necessary to take into account different factors for the choice of best satellite data, among these, the time of satellite pass with respect to the flood peak is the most important one. SAR data showed good results only for co-flood acquisitions, whereas multispectral images allowed detecting flooded areas also with the post-flood acquisition. With the support of ancillary data, it was possible to produce reliable geomorphological based flood maps in the study areas.
ARTICLE | doi:10.20944/preprints201807.0604.v1
Subject: Earth Sciences, Environmental Sciences Keywords: backscattering; L-band; SAR polarimetry; microwave; Chapman-Richards model; tropical forest
Online: 31 July 2018 (05:02:29 CEST)
Secondary forests (SF) are important carbon sinks, removing CO2 from the atmosphere through the photosynthesis process and storing photosynthates in their aboveground live biomass (AGB). This process occurring at large-scales partially counteracts C emissions from land-use change, playing, hence, an important role in the global carbon cycle. The absorption rates of carbon in these forests depend on forest physiology, controlled by environmental and climatic conditions as well as on the past land use, which is rarely considered for retrieving AGB from remotely sensed data. In this context, the main goal of this study is to evaluate the potential of full polarimetric ALOS-2 PALSAR-2 data for estimating AGB by taking into account the past-land use of SF areas in the Brazilian Amazon. We surveyed a chronosequence of 42 SF plots (20 ha) near the Tapajós National Forest in Pará state to quantifying AGB growth rates. We explored the full polarimetric data testing three regression models including non-linear (NL), multiple linear regressions models (MLR), and the semi-empirical extended water cloud model (EWCM). The results showed that the intensity of previous use has affected the structure of SF by reducing the AGB accumulation and being noticeable by several polarimetric attributes. The combination of multiple prediction variables with MLR improved the AGB estimation by 70% comparing amongst other models (R² adj. = 0.51; RMSE = 13.2 Mg ha-1) bias = 2.1 ± 37.9 Mg ha-1. The error propagation of the MLR model was estimated to be 15%.
ARTICLE | doi:10.20944/preprints201805.0116.v1
Subject: Engineering, Electrical & Electronic Engineering Keywords: Target recognition, SAR, keypoint, local descriptor, sparse representation, feature quantization, classification
Online: 7 May 2018 (11:47:13 CEST)
This paper considers target characterization and recognition in radar images with keypoint-based local descriptor. Most of the preceding works rely on the global features or raw intensity values, and hence produce the limited recognition performance. Moreover, the global features are sensitive to the real-world sources of variability, such as aspect view, configu-ration, and incidence angle changes, clutter, articulation, and occlusion. Keypoint-based local descriptor was developed as a powerful strategy to address invariance to contrast change and geometric distortion. This property inspires us to investigate whether the family of local features are relevant for radar target recognition. Most of the preceding works typically devote to finding the correspondences between a collected image and a reference one. The representative applications include image register and change detection. Little work was pursued to target recognition in SAR images. This is because the huge number of local descriptors resulting from radar images make the computational cost and memory consumption unacceptable. To handle the problems, this paper develops two families of methods. The proposed methods are used to achieve target recognition by means of local descriptors. Our first solver refers to building multiple linear regression models, and addresses the problem by the theory of sparse representation. The second scheme rebuilds a new feature by the feature quantization skill, from which the inference can be drawn. Multiple comparative studies are pursued to verify the performance of detectors and descriptors popularly used. The source code was publicly released on https://ganggangdong.github.io/homepage/.
ARTICLE | doi:10.20944/preprints201812.0227.v1
Subject: Earth Sciences, Other Keywords: digital aerial photogrammetry; SAR; model-assisted; biomass estimation; Copernicus; unmanned aerial vehicles
Online: 19 December 2018 (02:56:20 CET)
Due to the increasing importance of mangroves in climate change mitigation projects, more accurate and cost-effective aboveground biomass (AGB) monitoring methods are required. However, field measurement of AGB may be a challenge because of its remote location and the difficulty to walk in these areas. This study is based on the Livelihoods Fund’ Oceanium project of 10,000 hectare mangrove plantations monitoring. In a first step, the possibility of replacing traditional field measurements of sample plots in a young mangrove plantation by a semiautomatic processing of UAV-based photogrammetric point clouds was assessed. In a second step, Sentinel-1 radar and Sentinel-2 optical imagery were used as auxiliary information to estimate AGB and its variance for the entire study area under a model-assisted framework. AGB was measured using UAV imagery in a total of 95 sample plots. UAV plot data was used in combination with non-parametric Support Vector Regression (SVR) models for the estimation of the study area AGB using model-assisted estimators. Purely UAV-based AGB estimates and their associated standard error (SE) were compared with model-assisted estimates using (1) Sentinel-1, (2) Sentinel-2 and (3) a combination of Sentinel-1 and Sentinel-2 data as auxiliary information. The validation of the UAV-based individual tree height and crown diameter measurements showed a root mean square error (RMSE) of 0.21 m and 0.32 m respectively. Relative efficiency of the three model-assisted scenarios ranged between 1.61 and 2.15. Although all SVR models improved the efficiency of the monitoring over UAV-based estimates, the best results were achieved when a combination of Sentinel-1 and Sentinel-2 data was used. Results indicated that the methodology used in this research can provide accurate and cost-effective estimates of AGB in mangrove young plantations.
ARTICLE | doi:10.20944/preprints201804.0223.v4
Subject: Engineering, Marine Engineering Keywords: Gaofen-3; SAR; Wave Mode; calibration constants; cross-pol; noise floor; polarization
Online: 9 May 2018 (13:48:16 CEST)
In this paper, we analyze the measurements of the normalized radar cross-section(NRCS) in Wave Mode for Chinese C-band Gaofen-3(GF-3) synthetic aperture radar (SAR). Based on 2779 images from GF-3 quad-polarization SAR in Wave Mode and collocated wind vectors from ERA-Interim, we verify the feasibility of using ocean surface wind fields and VV-polarized NRCS to perform normalized calibration. The method uses well-validated empirical C-band geophysical model function (CMOD4) to estimate the calibration constant for each beam. The Amazon rainforest experiment results show that the accuracy of obtained calibration constant meets the requirements. In addition, the relationship between cross-pol NRCS and wind vectors is discussed. The cross-pol NRCS increases linearly with wind speed and it has an approximate cosine modulation with the wind direction when the wind speed is greater than 8m/s. The cross-polarized system noise floor is low enough to ignore it in wind retrieval. Furthermore, we also investigate the properties of the polarization ratio, denoted PR, and show that it is dependent on incidence angle and azimuth angle. Two empirical models of the PR are fitted, one as a function of incidence angle only, the other with additional dependence on azimuth angle. Assessments show that the σ_VV^0 retrieved from new PR models as well as σ_HH^0 is in good agreement with σ_VV^0 extracted from SAR images directly. And it is also shown that considering the azimuth angle can improve polarization conversion accuracy.
ARTICLE | doi:10.20944/preprints201710.0093.v1
Subject: Engineering, Automotive Engineering Keywords: SAR image; Visual attention model; Texture Saliency; Feature map; Focus of attention
Online: 13 October 2017 (17:08:14 CEST)
Targets detection in synthetic aperture radar (SAR) remote sensing images, which is a fundamental but challenging problem in the field of satellite image analysis, plays an important role for a wide range of applications and is receiving significant attention in recent years. Besides, the ability of human visual system to detect visual saliency is extraordinarily fast and reliable. However, computational modeling of SAR image scene still remains a challenge. This paper analyzes the defects and shortcomings of traditional visual models applied to SAR images. Then a visual attention model designed for SAR images is proposed. The model draws the basic framework of classical ITTI model; selects and extracts the texture features and other features that can describe the SAR image better. We proposes a new algorithm for computing the local texture saliency of the input image, then the model constructs the corresponding saliency maps of features; Next, a new mechanism of feature fusion is adopted to replace the linear additive mechanism of classical models to obtain the overall saliency map; Finally, the gray-scale characteristics of focus of attention (FOA) in saliency map of all features are taken into account, our model choose the best saliency representation, Through the multi-scale competition strategy, the filter and threshold segmentation of the saliency maps can be used to select the salient regions accurately, thereby completing this operation for the visual saliency detection in SAR images. In the paper, several types of satellite image data, such as TerraSAR-X (TS-X), Radarsat-2, are used to evaluate the performance of visual models. The results show that our model provides superior performance compared with classical visual models. By further contrasting with the classical visual models, Our model reduce the false alarm caused by speckle noise, and its detection speed is greatly improved, and it is increased by 25% to 45%.
ARTICLE | doi:10.20944/preprints201710.0059.v1
Subject: Keywords: average generalized ambiguity function; passive bistatic SAR; PSK modulating signal; spatial resolution
Online: 10 October 2017 (07:56:47 CEST)
The formula of the Generalized Ambiguity Function (GAF) of passive bistatic SAR system using the non-cooperative illuminators which transmit PSK modulating signals is derived to analyze the spatial resolution of the system. The average GAF is introduced to remove the effect of particular sequence of symbols on resolution because the particular sequence of symbols is usually unpredictable before being received. The influence of the waveform parameters of the PSK modulating signals, such as length of the symbol sequence and roll-off factor, on spatial resolution is investigated by numerical simulation. It is confirmed that the influence of the length of the symbol sequence and roll-off factor is very slight but still exists.
ARTICLE | doi:10.20944/preprints201609.0038.v2
Subject: Earth Sciences, Environmental Sciences Keywords: SAR offset and speckle tracking; glacier velocity; Radarsat-2 Wide Fine; Svalbard
Online: 10 September 2016 (05:03:14 CEST)
Glacier dynamics play an important role in the mass balance of many glaciers, ice caps and ice sheets. In this study we exploit Radarsat-2 (RS-2) Wide Fine (WF) data to determine the surface speed of Svalbard glaciers in the winters of 2012/2013 and 2013/2014 using Synthetic Aperture RADAR (SAR) offset and speckle tracking. The RS-2 WF mode combines the advantages of the large spatial coverage of the Wide mode (150 x 150 km) and the high pixel resolution (9m) of the Fine mode and thus has a major potential for glacier velocity monitoring from space through offset and speckle tracking. Faster flowing glaciers (1.95 m d-1 - 2.55 m d-1) which are studied in detail are Nathorstbreen, Kronebreen, Kongsbreen and Monacobreen. Using our Radarsat-2 WF dataset, we compare the performance of two SAR tracking algorithms, namely the GAMMA Remote Sensing Software and a custom written MATLAB script (GRAY method) that has primarily been used in the Canadian Arctic. Both algorithms provide comparable results, especially for the faster flowing glaciers and the termini of slower tidewater glaciers. A comparison of the WF data to RS-2 Ultrafine and Wide mode data reveals the superiority of RS-2 WF data over the Wide mode data.
ARTICLE | doi:10.20944/preprints202209.0169.v1
Subject: Earth Sciences, Geoinformatics Keywords: Synthetic Aperture Rader (SAR); Optical image (Sentinel 2); Random Forest (RF); CART; GEE
Online: 13 September 2022 (10:06:14 CEST)
Observing cultivated crops and other forms of land use is an important environmental and economic concern for agricultural land management and crop classification. Crop categorization offers significant crop management data, ensuring food security, and developing agricultural policies. Remote sensing data, especially publicly available Sentinel 1 and 2 data, has effectively been used in crop mapping and classification in cloudy places because of their high spatial and temporal resolution. This study aimed to improve crop type classification by combining Sentinel-1 (Synthetic Aperture Rader (SAR)) data and the Sentinel-2 Multispectral Instrument (MSI) data. In the study, Random Forest (RF) and Classification and Regression Trees (CART) classier were used to classify grain crops (Barley and Wheat). The classification results based on the combination of Sentinel-2 and Sentinel-1 data indicated an overall accuracy (OA) of 93 % and a kappa coefficient (K) of 0.896 for RF and (89.15%, 0.84) for the CART classifier. It is suggested to employ a mix of radar and optical data to attain the highest level of classification accuracy since doing so improves the likelihood that the details will be observed in comparison to the single-sensor classification technique and yields more accurate results.
Subject: Physical Sciences, Acoustics Keywords: SAR Interferometry; Accuracy; Big Data; Deformation Monitoring, Sentinel-1; Fading Signal; Signal Decorrelation
Online: 27 October 2020 (15:26:30 CET)
We scrutinize the reliability of multilooked interferograms for deformation analysis. Designing a simple approach in the evaluation of the accuracy of the estimated deformation signals, we reveal a prominent bias in the deformation velocity maps. The bias is the result of propagation of small phase error of multilooked interferograms through the time series and can sum up to 6.5 mm/yr in case of using the error prone short temporal baseline interferograms. We further discuss the role of the phase estimation algorithms in reduction of the bias and put recommend a unified intermediate InSAR product for achieving high-precision deformation monitoring.
Subject: Mathematics & Computer Science, Algebra & Number Theory Keywords: deep neural network; differentiable rendering; electromagnetic scattering modeling; ray tracing; SAR image simulation
Online: 9 October 2020 (16:24:29 CEST)
The calculation of echo intensity involved in the SAR image simulation is usually based on the electromagnetic formula or approximate formula derived under certain assumptions. However, the parameters used in these formulas are often difficult to obtain, and the formulas have errors with the actual situation. In this paper, a method of embedding deep neural network (DNN) into the simulation process based on ray tracing is proposed, so that the DNN model can be directly used to fit the calculation formula of echo intensity from real SAR images. Simulation results show that this method can obtain SAR images with high similarity.
ARTICLE | doi:10.20944/preprints201806.0136.v1
Subject: Chemistry, Organic Chemistry Keywords: Diplodia quercivora; oak; epi-epoformin; cyclohexeneoxide; etiolated wheat coleoptile bioassay; phytotoxicity; allelopathy; SAR
Online: 8 June 2018 (13:15:11 CEST)
(+)-epi-Epoformin (1), is a fungal cyclohexene epoxide isolated together with diplopimarane and sphaeropsidins A and C, a nor-ent-pimarane and two pimaranes, from the culture filtrates of Diplodia quercivora, a fungal pathogen for cork oak in Sardinia, Italy. Compound 1 possesses a plethora of biological activities including: antifungal, zootoxic and phytotoxic activity. The last activity and the peculiar structural feature of 1 suggested to carry out a structure activity relationship study, preparing eight key hemisynthetic derivatives and their phytotoxicity was assayed. The complete spectroscopic characterization and the activity in the etiolated wheat coleoptile bioassay of all the compounds is reported. Most of the compounds inhibited growth and some of them had comparable or higher activity than the natural product and the reference herbicide Logran. As regards the structure-activity relationship, the carbonyl proved to be essential for their activity of 1, as well as the conjugated double bond, while the epoxide could be altered with no significant loss.
ARTICLE | doi:10.20944/preprints201703.0179.v1
Subject: Engineering, Electrical & Electronic Engineering Keywords: GNSS-SAR; global navigation satellite system; synthetic aperture radar; range compression; range resolution
Online: 23 March 2017 (09:20:08 CET)
Passive Global Navigation Satellite System (GNSS)-based Synthetic Aperture Radar (SAR), known as GNSS-SAR, is a new passive radar imaging system. However, compared with conventional SAR, range resolution of GNSS-SAR is significantly lower. To improve range resolution of GNSS-SAR is an interested topic for investigation. In this paper, a novel range compression algorithm for enhancing range resolutions of GNSS-SAR is proposed. In the proposed scheme, at first, range compression is conducted by correlating the received reflected GNSS signal of intermediate frequency (IF) with the synchronized direct baseband GNSS signal in range domain. Then spectrum equalization is applied to the compressed results to suppress side lobes. Both theoretical analysis and simulation results have demonstrated that significant range resolution improvement in GNSS-SAR can be obtained by the proposed range compression algorithm, compared to the conventional range compression algorithm.
ARTICLE | doi:10.20944/preprints201608.0083.v1
Subject: Earth Sciences, Environmental Sciences Keywords: deformation; interferometry; geotechnical models; non‐linear problem; synthetic aperture radar (SAR); time‐series
Online: 8 August 2016 (14:50:19 CEST)
This paper is aimed at studying the temporal evolution of the surface displacements occurred over the past few years in the ocean-reclaimed platforms of the Shanghai megacity (China), which are mainly ascribable to consolidation processes of large dredger fills and alluvial deposits. With respect to previous analyses carried out over the same area, this work provides a joint multi-platform differential interferometry synthetic aperture radar (DInSAR) analysis, based on the application of the advanced Small BAseline Subset (SBAS) algorithm. This led us to retrieve long-term deformation time-series that are helpful for a better understanding of the on-going deformation phenomena. To this aim, we have exploited two sequences of SAR data collected by the ASAR/ENVISAT and by the COSMO-SkyMed (CSK) sensors, respectively, spanning the whole time period from 2007 to 2016. Unfortunately, the large time gap (of about three years) existing between the available ASAR/ENVISAT and CSK datasets gave rise to additional difficulties for their combination. Nevertheless, this problem has been faced by benefiting from the knowledge of a time-dependent model describing the temporal evolution of the expected deformations affecting the Shanghai ocean-reclaimed platforms.
ARTICLE | doi:10.20944/preprints202102.0520.v1
Subject: Life Sciences, Biochemistry Keywords: Meprin B; Meprin beta; metalloproteinase; astacin; hydroxamate; SAR (structure activity relationship); MWT-S-270
Online: 23 February 2021 (14:27:53 CET)
The astacin protease Meprin β represents an emerging target for drug development due to its potential involvement in disorders such as acute and chronic kidney injury and fibrosis. Here, we elaborate on the structural basis of inhibition by a specific Meprin β inhibitor. Our analysis of the crystal structure suggests different binding modes of the inhibitor to the active site. This flexibility is caused, at least in part, by movement of the C-terminal region of the protease domain (CTD). The CTD movement narrows the active site cleft upon inhibitor binding. Compared with other astacin proteases, among those the highly homologous isoenzyme Meprin α, differences in the subsites account for the unique selectivity of the inhibitor. Although the inhibitor shows substantial flexibility in the orientation within the active site, the structural data as well as binding analyses including molecular dynamics simulations support a contribution of electrostatic interactions, presumably by arginine residues, to binding and specificity. Collectively, the results presented here and previously support an induced fit and substantial movement of the CTD upon ligand binding and, possibly, during catalysis. To the best of our knowledge, we here present the first structure of a Meprin β holoenzyme containing a zinc ion and a specific inhibitor bound to the active site. The structural data will guide rational drug design and the discovery of highly potent Meprin inhibitors.
ARTICLE | doi:10.20944/preprints202010.0187.v1
Subject: Keywords: sodium adsorption ratio; SAR; CROSS, electrical conductivity; specific conductivity; salinity; irrigation; groundwater; water quality
Online: 9 October 2020 (08:45:56 CEST)
Soil water loss by evaporation influences the sodium adsorption ratio (SAR) of irrigation drainage water. Evaporation concentrates sodium and magnesium but calcite precipitation has a more complicated effect on soluble calcium and alkalinity. Here we propose a revised sodicity hazard assessment that quantifies the impact of evaporative water loss and calcite precipitation on drainage water SAR. This paper shows sodicity hazard is determined by the initial composition of irrigation water as originally suggested by previous researchers, and provide a simple, accurate way to identify the potential sodicity hazard of any irrigation water. In particular, the initial equivalent concentration of alkalinity and calcium determine the salinization pathway followed during evaporation. If the irrigation water alkalinity exceeds soluble calcium expressed as equivalent concentrations, drainage water SAR approaches an upper limit determined by the initial relative concentration of sodium and magnesium. If irrigation water alkalinity is less than soluble calcium, drainage water SAR approaches a lower limit determined by the initial calcium, magnesium and sodium. In both cases the SAR is scaled by the square root of the concentration factor √Fc quantifying soil water loss. To assess the impact of evaporation and calcite precipitation on the SAR and test the accuracy of the new sodicity hazard assessment, we evaluated data from previously published lysimeter studies. We plotted water composition boundaries for each source water, comparing these boundaries to the drainage water composition recorded in the lysimeter studies. As salinity increased by evaporation, each drainage water followed a distinct salinization path.
ARTICLE | doi:10.20944/preprints202003.0471.v1
Subject: Engineering, Marine Engineering Keywords: search and rescue; optimal search algorithm; BFOA; multi-direction search; co-ordinate SAR operations
Online: 31 March 2020 (23:26:00 CEST)
Enhancing the effectiveness of search and rescue operation at sea is always a duty of utmost importance of the coastal states. The search area for distressed objects can be determined by using Monte Carlo simulation, combined with the Median-Filter. Once the search area has been identified, the success of search and rescue operations depends on the sweeping ability of search and rescue vessel at the probability area of the distress object with the minimum time. This is the important element to the success of the search and rescue operation as it minimizes the risk and cost for Search and rescue team. In this article, the authors study and propose the use of Bacterial Foraging Optimization Algorithm (BFOA) to calculate the optimal search and co-ordination route for many search and rescue vessels in Vietnam sea. The simulation results show that it is quite consistent with reality and BFOA can be effectively applied to determine a quick search.
ARTICLE | doi:10.20944/preprints201808.0066.v1
Subject: Earth Sciences, Geoinformatics Keywords: Crop classification; SAR; Optical; time series; Sentinel-1; Sentinel-2; random forest; machine learning
Online: 3 August 2018 (12:01:50 CEST)
A timely inventory of agricultural areas and crop types is an essential requirement for ensuring global food security. Satellite remote sensing has proven to be an increasingly more reliable tool to identify crop types. With the Copernicus program and its Sentinel satellites, a growing source of satellite remote sensing data is publicly available at no charge. Here we use joint Sentinel-1 radar and Sentinel-2 optical imagery to create a crop map for Belgium. To ensure homogenous radar and optical input across the country, Sentinel-1 12-day backscatter composites were created after incidence angle normalization, and Sentinel-2 NDVI images were smoothed to yield dekadal cloud-free composites. An optimized random forest classifier predicted the 8 crop types with a maximum accuracy of 82% and a kappa coefficient of 0.77. We found that a combination of radar and optical imagery always outperformed a classification based on single-sensor inputs, and that classification performance increased throughout the season until July, when differences between crop types are largest. Furthermore we showed that the concept of classification confidence derived from the random forest classifier provided insight in the reliability of the predicted class for each pixel, clearly showing that parcel borders have a lower classification confidence. We concluded that the synergistic use of radar and optical data for crop classification led to richer information increasing classification accuracies compared to optical-only classification. Further work should focus on object-level classification and crop monitoring to exploit the rich potential of combined radar and optical observations.
ARTICLE | doi:10.20944/preprints201807.0120.v1
Subject: Engineering, Electrical & Electronic Engineering Keywords: SAR system; efficient focusing of SAR data; Inverse problem; radar theory; remote sensing; SAR data focusing; phase shifts; satellite trajectory; spatial resolution; synthetic aperture radar; Geometry; Satellites; Ancillary Data; Singular Value Decomposition; Blind deconvolution; Signal Processing; Parameter estimation; Algorithm; Imaging; Phase estimation; Phase compensation; Computational modeling; Image resolution; Synthetic Aperture
Online: 6 July 2018 (15:29:21 CEST)
Synthetic Aperture RADAR (SAR) is a radar imaging technique in which the relative motion of the sensor is used to synthesize a very long antenna and obtain high spatial resolution. Standard SAR raw data processing techniques assume uniform motion of the satellite (or aerial vehicle) and a fixed antenna beam pointing sideway orthogonally to the motion path, assumed rectilinear. Despite SAR data processing is a well established imaging technology that has become fundamental in several fields and applications, in this paper a novel approach has been used to exploit coherent illumination, demonstrating the possibility of extracting a large part of the ancillary data information from the raw data itself, to be used in the focusing procedure. In this work an effort has been carried out to try to focus the raw SAR complex data matrix without the knowledge of anyof the parameters needed in standard focusing procedures as Range Doppler (RD) algorithm, ω - K algorithm and Chirp Scaling (CS) algorithm. All the literature references regarding the algorithms needed to obtain a precise image from raw data use such parameters that refer both to the SAR system acquisition geometry and its radiometric specific parameters. In , authors introduced a preliminary work dealing with this problem and able to obtain, in the presence of a strong point scatterer in the observed scene, good quality images, if compared to the standard processing techniques. In this work the proposed technique is described and performances parameters are extracted to compare the proposed approach to RD.
ARTICLE | doi:10.20944/preprints202206.0013.v1
Subject: Earth Sciences, Geoinformatics Keywords: SAR Interferometry (InSAR); Digital Elevation Models (DEM); Neural Networks; DEM Fusion; ICESat-2 spaceborne altimetry
Online: 1 June 2022 (10:11:48 CEST)
Interferometry Synthetic Aperture Radar (InSAR) is an advanced remote sensing technique for studying the earth's surface topography and deformations. It is used to generate high-quality Digital Elevation Models (DEMs). DEMs are a crucial and primary input to various topographical quantification and modelling applications. The quality of input DEMs can be further improved using fusion methods, which combine multi-sensor or multi-temporal datasets intelligently to retrieve the best information amongst the input data. This research study is based on developing a Neural Network based fusion approach for improving InSAR based DEMs in plain and hilly terrains. The study areas comprise of relatively plain terrain from Ghaziabad and hilly terrain of Dehradun and their surrounding regions. The training dataset consists of DEM elevations and derived topographic attributes like slope, aspect, topographic position index (TPI), terrain ruggedness index (TRI), and vector roughness measure (VRM) in different land use land cover classes of the study areas. The spaceborne altimetry ICESat-2 ATL08 photon data is used as a reference elevation. A Feed Forward Neural Network with backpropagation algorithm is trained based on the prepared training samples. The trained model produces fused DEMs by learning the relationship between the input and target samples. This is used to predict elevations in the test areas. The accuracy of results from the models are assessed with TanDEM-X 90 m DEM. The fused DEMs show significant improvement in terms of RMSE over the input DEMs with improvement factor of 94.65 % in plain area and 82.62 % in hilly area. The study concludes that the ANN with its universal approximation property is able to significantly improve the fused DEM.
ARTICLE | doi:10.20944/preprints202105.0784.v1
Subject: Chemistry, Analytical Chemistry Keywords: Quinoline; Isoquinoline; G-Quadruplex ligands; k-RAS; c-MYC; telomere; SAR; pyri-dine-dicarboxamide; molecular dynamics
Online: 31 May 2021 (14:03:28 CEST)
Quadruplex-interactive small molecules have a wide potential application, not only as drugs but also as sensors of quadruplexes structures. The purpose of this work is the synthesis of analogues of the bis-methylquinolinium-pyridine-2,6-dicarboxamide G4 ligand 360A, to identify relevant structure-activity relationships to apply to the design of other G4-interactive small molecules bearing bis-quinoline or bis-isoquinoline moieties. Thermal denaturation experiments revealed that non-methylated derivatives with a relative 1,4 position between the amide linker and the nitrogen of the quinoline ring are moderate G4 stabilizers, with a preference for the hybrid h-Telo G4. Insertion of a positive charge upon methylation of quinoline/isoquinoline nitrogen increases compounds capacity to selectively stabilize G4s compared to duplex DNA, with a preference for parallel structures. Among these, compounds having a relative 1,3-position between the charged methylquinolinium/isoquinolinium nitrogen and the amide linker are the best G4 stabilizers. More interestingly, these ligands showed different capacities to selectively block DNA polymer-ization in a PCR-stop assay and to induce G4 conformation switches of hybrid h-Telo G4. Mo-lecular dynamic simulations with the parallel k-RAS G4 structure showed that the relative spatial orientation of the two methylated quinoline/isoquinoline rings determines the ligands mode and strength of binding to G4s.
ARTICLE | doi:10.20944/preprints201905.0030.v1
Subject: Mathematics & Computer Science, Artificial Intelligence & Robotics Keywords: transfer learning; convolutional neural network; electro-optical imaging; synthetic aperture radar (SAR) imaging; optimal transport metric
Online: 6 May 2019 (06:28:04 CEST)
Reemergence of deep Neural Networks (CNNs) has lead to high-performance supervised learning algorithms for the Electro-Optical (EO) domain classification and detection problems. This success is possible because generating huge labeled datasets has become possible using modern crowdsourcing labeling platforms such as Amazon Mechanical Turk that recruit ordinary people to label data. Unlike the EO domain, labeling the Synthetic Aperture Radar (SAR) domain data can be a lot more challenging and for various reasons using crowdsourcing platforms is not feasible for labeling the SAR domain data. As a result, training deep networks using supervised learning is more challenging in the SAR domain. In the paper,we present a new framework to train a deep neural network for classifying Synthetic Aperture Radar (SAR) images by eliminating the need for huge labeled dataset. Our idea is based on transferring knowledge from a related EO domain problem, where labeled data is easy to obtain. We transfer knowledge from the EO domain through learning a shared invariant cross-domain embedding space that is also discriminative for classification. To this end, we train two deep encoders that are coupled through their last year to map data points from the EO and the SAR domains to the shared embedding space such that the distance between the distributions of the two domains is minimized in the latent embedding space. We use the Sliced Wasserstein Distance (SWD) to measure and minimize the distance between these two distributions and use a limited number of SAR label data points to match the distributions class-conditionally. As a result of this training procedure, a classifier trained from the embedding space to the label space using mostly the EO data would generalize well on the SAR domain. We provide theoretical analysis to demonstrate why our approach is effective and validate our algorithm on the problem of ship classification in the SAR domain by comparing against several other learning competing approaches.
ARTICLE | doi:10.20944/preprints202301.0231.v1
Subject: Earth Sciences, Geology Keywords: NDVI; SAR; change detection; Norway; Sentinel-1; Sentinel-2; deep learning; U-Net; CCDC; Google Earth Engine
Online: 13 January 2023 (02:00:25 CET)
Landslide risk mitigation is limited by data scarcity. This could be improved using continuous landslide detection systems. In order to investigate which image types and machine learning (ML) models are most useful for landslide detection in a Norwegian setting, we compared the performance of five different ML models, for the Jølster case study (30-July-2019), in Western Norway. These included three globally pre-trained models; i) the Continuous Change Detection and Classification (CCDC) algorithm, ii) a combined k-means clustering and Random Forest classification model, and iii) a convolutional neural network (CNN), and two locally-trained models, including; iv) Classification and Regression Trees and v) a U-net CNN model. Images used included Sentinel-1, Sentinel-2, digital elevation model (DEM) and slope. The globally-trained models performed poorly in shadowed areas, and were all outperformed by the locally-trained models. A maximum Matthew’s correlation coefficient (MCC) score of 89% was achieved with model v, using combined Sentinel-1 and -2 images as input. This is one of the first attempts to apply deep-learning to detect landslides with both Sentinel-1 and -2 images. Using Sentinel-1 images only, the locally-trained deep-learning model significantly outperformed the conventional ML model. These findings contribute towards developing a national continuous monitoring system for landslides.
Subject: Earth Sciences, Atmospheric Science Keywords: Surface soil moisture; Sentinel-1 SAR; Sentinel-2; Vegetation water content; Water cloud model; Support vector regression
Online: 2 June 2021 (15:22:42 CEST)
Surface soil moisture (SSM) is a significant factor affecting crop growth. This paper presents a method for retrieving SSM over wheat-covered areas using synergy dual-polarization C-band Sentinel-1 synthetic aperture radar and Sentinel-2 optical data. Firstly, a modified water cloud model (WCM) was proposed to remove the influence of vegetation from the backscattering coefficient of the radar data. The vegetation fraction was then introduced in this WCM, and the vegetation water content (VWC) was calculated using multiple linear regression model. Subsequently, the support vector regression technique was used to retrieve the SSM. This approach was validated using in-situ measurements of the wheat field in Hebi, in the north of Henan Province. The key findings of this study are as follows: (1) Based on vegetation indices obtained from Sentinel-2; the proposed VWC estimation model can effectively eliminate the influence of vegetation; (2) compared with vertical transmit and horizontal receive polarization, vertical transmit and vertical receive polarization is better for detecting changes in SSM at different growth stages of wheat; and, (3) the validation results indicated that the proposed approach, based on Sentinel-1 and Sentinel-2 data, successfully retrieved SSM in the study area.
ARTICLE | doi:10.20944/preprints201809.0550.v1
Subject: Engineering, Electrical & Electronic Engineering Keywords: synthetic aperture radar (SAR); real-time processing; single FPGA node imaging processing; multi-node parallel accelerating technique
Online: 27 September 2018 (15:14:49 CEST)
With the development of satellite load technology and very-large-scale integrated (VLSI) circuit technology, on-board real-time synthetic aperture radar (SAR) imaging systems have facilitated rapid response to disasters. Limited by severe size, weight, and power consumption constraints, a key challenge of on-board SAR imaging system design is to achieve high real-time processing performance. In addition, with the rise of multi-mode SAR applications, the reconfiguration of the on-board processing system is beginning to receive widespread attention. This paper presents a multi-mode SAR imaging chip with SoC architecture based on the reconfigurable double-operation engines and multilayer switching network. We decompose the commonly used extend chirp scaling (CS) SAR imaging algorithm into 8 types of double-operation engines according to the computing orders, and design a three-level switching network to connect these engines for data transition. The CPU is responsible for engine scheduling based on data flow driven with instructions to implement each part of the CS algorithm. Thus, multi-mode floating-point SAR imaging processing can be integrated into a single Application-Specific Integrated Circuit (ASIC) chip instead of relying on distributed technologies. As a proof of concept, a prototype measurement system with chip-included board is implemented, and the performance of the proposed design is demonstrated on Chinese Gaofen-3 stripmap continuous imaging. A chip requires 9.2 s, 50.6 s and 7.4 s for a stripmap with 16,384×16,384 granularity, multi-channel stripmap with 65.536×8192 granularity and multi-channel scan mode with 32,768×4096 granularity and 6.9 W for the system hardware to process the SAR raw data.
ARTICLE | doi:10.20944/preprints201808.0359.v1
Subject: Engineering, Electrical & Electronic Engineering Keywords: Mutual coupling suppression; slotted array antennas; synthetic aperture radar (SAR); Multiple-Input Multiple-Output (MIMO) systems; decoupling method
Online: 20 August 2018 (14:46:43 CEST)
This paper presents a new approach to suppress interference between neighbouring radiating elements resulting from surface wave currents. The proposed technique will enable the realization of low-profile implementation of highly dense antenna configuration necessary in SAR and MIMO communication systems. Unlike other conventional techniques of mutual coupling suppression where decoupling slab is located between the radiating antennas the proposed technique is simpler and only requires embedding linear slots near the periphery of the patch. Attributes of this technique are (i) significant improvement in the maximum isolation between the adjacent antennas by 26.7 dB in X-band, & >15 dB in Ku and K-bands; (ii) reduction in edge-to-edge gap between antennas to 10 mm (0.37λ); and (iii) improvement in gain by >40% over certain angular directions, which varies between 4.5 dBi to 8.2 dBi. The proposed technique is simple to implement at low-cost.
ARTICLE | doi:10.20944/preprints201807.0390.v1
Subject: Earth Sciences, Space Science Keywords: SAR remote sensing, Optical remote sensing, RISAT-1, LISS III, RVI, VI, cotton, height, LAI, Biomass, Vegetation water content
Online: 20 July 2018 (14:56:07 CEST)
Morphological parameters like cotton height, branches, Leaf Area Index and biomass are mainly affected by the vegetation water content (VWC). Periodical assessment of the VWC and crop parameters is required for timely management of the crop for maximizing yield. The study aimed at using both optical and microwave remotely sensed data to assess cotton crop condition based on the above mentioned traits. Vegetation indices (VI) derived from ground based measurements (5 narrow band and 2 broad band VIs) as well as satellite derived reflectance (2 broad band VIs) were assessed. Regression models were derived for estimating LAI, biomass and plant water content using the ground based indices and applied to the satellite derived spectral index (from LISS-III) map to estimate the respective parameters. HH and HV polarization from RISAT-1 were used to derive Radar Vegetation Index (RVI). The coefficient of determination of the model for estimating LAI, biomass and vegetation water content of cotton with optical vegetation index as input parameter were found to be 0.42, 0.51 and 0.52, respectively. The correlation between RVI and plant height, date of planting in terms of the age of the crop and vegetation water content were found to range between 0.4 to 0.6. The fresh biomass from RVI showed spatial variability from 100 gm-2 to 4000 gm-2 while the dry biomass map derived from NDVI showed spatial variability of 50 to 950 g m-2 for the study area. Plant water content in the district varied from 65 to 85%. The correlation between optical vegetation index and RVI was not significant. Hence a multiple linear regression model using both optical index (NDVI and LSWI) and SAR index (RVI) was developed to assess the LAI, biomass and plant water content. The model showed a R2 of 0.5 for LAI estimation but not significant for biomass and water content. This study show cased the use of combined optical and microwave (C band) remote sensing for cotton condition assessment.
REVIEW | doi:10.20944/preprints202301.0099.v1
Subject: Life Sciences, Cell & Developmental Biology Keywords: Atg8; autophagic receptor; Cue5; SAR; selective autophagy; selective autophagy receptor; ubiquitin; ubiquitin-binding domain; ubiquitin-binding protein; ubiquitin-binding receptor
Online: 5 January 2023 (04:33:12 CET)
The selectivity in selective autophagy pathways is achieved via the selective autophagy receptors (SARs) – proteins that bind a ligand on the substrate to be degraded and the Atg8-family protein on the growing autophagic membrane, phagophore, effectively bridging them. In mammals, the most common ligand of SARs is ubiquitin, a small protein modifier that tags substrates for their preferential degradation by autophagy. Consequently, the most common SARs are the ubiquitin-binding SARs, such as SQSTM1/p62 (sequestosome 1). Surprisingly, there is only one SAR of this type in yeast – Cue5, which acts as a receptor for aggrephagy and proteaphagy – pathways that remove the ubiquitinated protein aggregates and proteasomes, respectively. However, recent studies described the ubiquitin-dependent autophagic pathways that do not require Cue5, e.g. stationary phase lipophagy for intracellular lipid droplets and nitrogen starvation-induced mitophagy for mitochondria. What is the role of ubiquitin in these pathways? Here, we propose that the ubiquitinated lipid droplets and mitochondria are recognized by the alternative ubiquitin-binding SARs. Our analysis identifies the proteins that could potentially fulfill this role in yeast. We think that matching of the ubiquitin-dependent (but Cue5-independent) autophagic pathways with the ubiquitin-and-Atg8-binding proteins enlisted here might uncover the novel ubiquitin-binding SARs in yeast.
ARTICLE | doi:10.20944/preprints202111.0189.v1
Subject: Earth Sciences, Environmental Sciences Keywords: InSAR; TanDEM-X; forest degradation; biomass change; Synthetic Aperture Radar; SAR; Carbon cycle; Satellite data; Earth Observation; DLR; X-band
Online: 9 November 2021 (15:43:37 CET)
Current satellite remote sensing methods struggle to detect and map forest degradation, a critical issue as it is likely a major and growing source of carbon emissions and biodiveristy loss. TanDEM-X InSAR phase height (hϕ) is a promising variable for measuring forest disturbances, as it is closely related to mean canopy height, and thus should decrease if canopy trees are removed. However, previous research has focused on relatively flat terrain, despite the fact that much of the worlds’ remaining tropical forests are found in hilly areas, and this inevitably introduces artifacts in sideways imaging systems. In this paper, we find a relationship between hϕ and aboveground biomass change in four selectively logged plots in a hilly region of central Gabon. We show that minimising the level of multilooking in the interferometric processing chain strengthens this relationship, and that degradation estimates across steep slopes in the surrounding region are improved by selecting data from the most appropriate pass directions on a pixel-by-pixel basis. This shows that TanDEM-X InSAR can measure the magnitude of degradation, and that topographic effects can be mitigated if data from multiple SAR viewing geometries are available.
Subject: Biology, Anatomy & Morphology Keywords: COVID-19; Severe acute respiratory syndrome (SARS); SAR-CoV-2; flow cytometry; flow virometry; viral particles; herpes simplex virus; HSV; nanoparticles
Online: 12 May 2020 (07:45:53 CEST)
The coronavirus disease caused by SARS-CoV-2 (known as COVID 19) is highly contagious and has spread rapidly over 200 countries over last three months. WHO suggested urgent escalation in testing, isolation and contact tracing, as the "backbone" of managing the pandemic. Globally, the detection of SARS-CoV-2 in patients are done by RT-PCR and blood antibody-based testing. In addition to the existing processes, flow-cytometry could be used as a high-throughput and efficient diagnostic method for detection of COVID 19. The suspected COVID 19 samples can be analyzed using ‘Indirect flow cytometry’, with the specific primary antibody and fluorescent tagged secondary antibodies. The fluorescence signal can distinguish the infected v/s non-infected samples. In the present article, we have summarized the applications of Flow virometry to study various viruses and have proposed possible application in the detection of COVID 19.
ARTICLE | doi:10.20944/preprints201901.0050.v1
Subject: Earth Sciences, Geoinformatics Keywords: mapping cocoa agroforests; Congo Basin rainforest; sentinel-1; SAR; GLCM textures; grey level quantization; random forest algorithm; machine learning; classification uncertainty
Online: 7 January 2019 (09:56:10 CET)
Delineating the cropping area of cocoa agroforests is a major challenge for quantifying the contribution of the land use expansion to tropical deforestation. Discriminating cocoa agroforests from tropical transition forests using multi-spectral optical images is difficult due to a similarity in the spectral characteristics of their canopy; moreover, optical sensors are largely impeded by the frequent cloud cover in the tropics. This study explores multi-season Sentinel-1 C-band SAR image to discriminate cocoa agroforests from transition forests for a heterogeneous landscape in central Cameroon. We use an ensemble classifier, random forest, to average SAR image texture features of GLCM (Grey Level Co-occurrence Matrix) across seasons; next, we compare classification performance with results from RapidEye optical data. Moreover, we assess the performance of GLCM texture feature extraction at four different grey level quantization: 32bits, 8bits, 6bits, and 4bits. The classification overall accuracy (OA) of texture-based maps outperformed that from an optical image; the highest OA of 88.8% was recorded at 6bits grey level. This quantization level, in comparison to the initial 32bits in SAR images, reduced the class prediction error by 2.9%. Although this prediction gain may be large for the landscape area, the resultant thematic map reveals the decrease and fragmentation of forest cover by cocoa agroforests. According to our classification validation, the Shannon entropy (H) or uncertainty provides a reliable validation for class predictions and reveals detail inference for discriminating inherently heterogeneous vegetation categories. The texture-based classification achieved a reliable accuracy considering the heterogeneity of the landscape and vegetation classes.
ARTICLE | doi:10.20944/preprints202110.0363.v1
Subject: Engineering, Other Keywords: Oil spills; synthetic aperture radar (SAR); deep convolutional neural networks (DCNNs); vision transformers (ViTs); deep learning; semantic segmentation; marine pollution; remote sensing
Online: 25 October 2021 (15:42:36 CEST)
Oil spillage over a sea or ocean’s surface is a threat to marine and coastal ecosystems. Spaceborne synthetic aperture radar (SAR) data has been used efficiently for the detection of oil spills due to its operational capability in all-day all-weather conditions. The problem is often modeled as a semantic segmentation task. The images need to be segmented into multiple regions of interest such as sea surface, oil spill, look-alikes, ships and land. Training of a classifier for this task is particularly challenging since there is an inherent class imbalance. In this work, we train a convolutional neural network (CNN) with multiple feature extractors for pixel-wise classification; and introduce to use a new loss function, namely ‘gradient profile’ (GP) loss, which is in fact the constituent of the more generic Spatial Profile loss proposed for image translation problems. For the purpose of training, testing and performance evaluation, we use a publicly available dataset with selected oil spill events verified by the European Maritime Safety Agency (EMSA). The results obtained show that the proposed CNN trained with a combination of GP, Jaccard and focal loss functions can detect oil spills with an intersection over union (IoU) value of 63.95%. The IoU value for sea surface, look-alikes, ships and land class is 96.00%, 60.87%, 74.61% and 96.80%, respectively. The mean intersection over union (mIoU) value for all the classes is 78.45%, which accounts for a 13% improvement over the state of the art for this dataset. Moreover, we provide extensive ablation on different Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) based hybrid models to demonstrate the effectiveness of adding GP loss as an additional loss function for training. Results show that GP loss significantly improves the mIoU and F1 scores for CNNs as well as ViTs based hybrid models. GP loss turns out to be a promising loss function in the context of deep learning with SAR images.
ARTICLE | doi:10.20944/preprints201712.0018.v1
Subject: Chemistry, Food Chemistry Keywords: non-acylated anthocyanins; anthocyanins with aromatic acylation; SAR; mahaleb cherry; blackcurrant; black carrot; ‘Sun Black’ tomato; VCAM-1; ICAM-1; endothelial adhesion molecules
Online: 4 December 2017 (07:04:17 CET)
Anthocyanins, the naturally occurring pigments responsible for most red to blue colours of flowers, fruits and vegetables, have also attracted interests because of their potential health effects. With the aim of contributing to major insights into their structure-activity relationship (SAR), we have evaluated the radical scavenging and biological activities of selected purified anthocyanin samples (PASs) from various anthocyanin-rich plant materials: two fruits (mahaleb cherry and blackcurrant) and two vegetables (black carrot and ‘Sun Black’ tomato). PASs from the above-mentioned plant material have been evaluated for their antioxidant capacity, using TEAC and ORAC assays. In human endothelial cells, we analysed the biological activity of different PASs by measuring their effects on the expression of endothelial inflammatory markers, including endothelial adhesion molecules VCAM-1 and ICAM-1. We demonstrated that all the different PASs showed biological activity. They exhibited antioxidant capacity of different magnitude, higher for samples containing non-acylated anthocyanins (typical for fruits) compared to samples containing more complex anthocyanins acylated with cinnamic acid derivatives (typical for vegetables), even though this order was slightly reversed when ORAC assay values were expressed on molar basis. Concordantly, PASs containing non-acylated anthocyanins reduced the expression of endothelial inflammatory antigens more than samples with aromatic acylated anthocyanins, suggesting the potential beneficial effect of structurally diverse anthocyanins in cardiovascular protection.
ARTICLE | doi:10.20944/preprints202211.0243.v1
Subject: Engineering, Electrical & Electronic Engineering Keywords: deep neural network (DNN); synthetic aperture radar automatic target recognition (SAR-ATR); universal adversarial perturbation (UAP); U-Net; attention heatmap; layer-wise relevance propagation (LRP)
Online: 14 November 2022 (06:37:35 CET)
Recent studies have proven that synthetic aperture radar (SAR) automatic target recognition (ATR) models based on deep neural networks (DNN) are vulnerable to adversarial examples. However, existing attacks are easily failed in the case where adversarial perturbations cannot be fully fed to victim models. We call this situation perturbation offset. Moreover, since background clutter takes up most of the areas in SAR images and has low relevance to recognition results, fooling models with global perturbations is quite inefficient. This paper proposes a semi-whitebox attack network, called Universal Local Adversarial Network (ULAN), to generate universal adversarial perturbations (UAP) for the target regions of SAR images. In the proposed network, we calculate the model’s attention heatmaps through layer-wise relevance propagation (LRP), which is used to locate the target regions of SAR images that have high relevance to recognition results. In particular, we utilize a generator based on the U-Net to learn the mapping from noise to UAPs and craft adversarial examples by adding the generated local perturbations to target regions. Experiments indicate that the proposed method fundamentally prevents perturbation offset and achieves comparable attack performance to conventional global UAPs by perturbing only a quarter or less of SAR image areas.
ARTICLE | doi:10.20944/preprints202104.0621.v1
Subject: Life Sciences, Virology Keywords: SAR-COV-2; COVID-19; Asymptomatic patients; Viral transmission networks; Exponential random graph model (ERGM) network analysis; Demographic homogeneities and heterogeneities; Symptomological homogeneities and heterogeneities
Online: 22 April 2021 (21:10:10 CEST)
Background Hokkaido is the northernmost, least populous, and coldest of the Japanese islands. It was the first prefecture to be affected by COVID-19, while Kanagawa is home to one of the most populous areas of Japan, namely the Tokyo metro area. The Japanese government responded early during the pandemic by identifying infected patients, contact tracing, and performing PCR analysis on anyone who was suspected of having been exposed to SARS-CoV-2. The government has also been publishing information about each individual who tested positive for the virus. Both Hokkaido and Kanagawa started recording COVID-19 cases in the winter of 2020 and have detailed records of thousands of patients, thus providing an invaluable resource for the transmission and behavior of the virus. Methods The current study analyzed the COVID-19 registry data from the Hokkaido and Kanagawa prefectures. The Hokkaido registry contained 1,269 cases (674 (53%) females and 595 (47%) males) recorded between February 14 and July 22, 2020. The Kanagawa registry had 3,123 cases (1,346 (43%) females and 1,777 (57%) males. The final data contained a total of 4,392 cases (2,020 (46%) females and 2,372 (54%) males). By leveraging the information on viral transmission paths available in the registry data, we performed exponential random graph model (ERGM) network analysis to examine demographic and symptomological homophilies of the SARS-CoV-2 viral transmission networks. Results We observed age, symptomatic, and asymptomatic homophilies in both prefectures. Furthermore, those patients who contracted the virus through secondary or tertiary contacts were more likely to be asymptomatic than those who contracted it from primary infection cases. The transmission networks showed that transmission occurred significantly in healthcare settings, as well as in families, although the size of the networks was small in the latter. Most of the transmissions stopped at the primary and secondary levels and no transmission beyond quaternary was observed. We also observed a higher level of asymptomatic transmission in Kanagawa than in Hokkaido. Conclusions Symptom homophilies are an important component of COVID-19 and suggest that nuanced genetic differences in the virus may affect its epithelial cell type range and can thus result in the diversity of symptoms seen in individuals infected by SARS-CoV-2. Moreover, environmental variables such as temperature and humidity may also be playing an important role in the overall pathogenesis of the virus.
ARTICLE | doi:10.20944/preprints201905.0260.v1
Subject: Earth Sciences, Oceanography Keywords: Synthetic aperture radar (SAR); along-track interferometry (ATI); sub-pixel offset tracking (sPOT); COSMO-SkyMed (CSK); staring spotlight (ST); micro-motion (m-m); vibrations; frequency modes
Online: 21 May 2019 (11:33:59 CEST)
This research aims to estimate the micro-motion (m-m) of ships. The problem of motion and m-m detection of targets is usually solved using synthetic aperture radar (SAR) along-track interferometry (ATI) which is observed employing two radars spatially distanced by a baseline extended in the azimuth direction. This paper is proposing a new approach where the m-m estimation of ships, occupying thousands of pixels, is measured processing the information given by sub-pixel tracking generated during the coregistration process of several re-synthesized time-domain and overlapped sub-apertures. The SAR products are generated splitting the raw data, according to a small-temporal baseline strategy, observed by one single wide-band staring spotlight (ST) SAR image. The predominant vibrational modes of different ships are estimated and results are promising to extend this application in performing surveillance also of land-based industries activities. Experiments are performed processing one ST SAR image observed by the COSMO-SkyMed satellite system.
ARTICLE | doi:10.20944/preprints201607.0070.v1
Subject: Behavioral Sciences, Other Keywords: Self-Action Leadership (SAL), SAL model, SAL theory, nomological, existential growth, organizational (or corporate) citizen, SAR project, SAL project, step-habit, Self-Declaration of Independence, Self-Constitution
Online: 23 July 2016 (10:26:10 CEST)
In 2015, the Self-Action Leadership Theory—a qualitative, nomological expansion of self-leadership theory rooted in atmospheric and astronomical metaphor aimed at expanding the personal freedom of individuals, organizations, and nations by bolstering the existential growth of individuals through a series of Maslow-esque stages of holistic, personal development. This article introduces an accompanying, practitioner-based Model of Self-Action Leadership (SAL) aimed at the implicit enhancement of a holistic range of administrative processes through explicit training, mentoring, and coaching in the model’s general and universally-applicable principles and practices. The SAL model produces an original construct of personal leadership practice that builds upon the extant self-leadership academic canon, which dates back to 1983 (Manz, 1983). It also provides an analogue to four of the five core processes of Project Management by positioning a self-action leader (an individual) as the ongoing “project” at hand. The SAL Model is rooted in action research and was developed through a variety of self-oriented, action research projects in conjunction with a comprehensive, qualitative, analytical autoethnographic study of a scholar’s life experiences.