PreprintArticleVersion 2Preserved in Portico This version is not peer-reviewed
AutoCloud+, a “Universal” Physical and Statistical Model-Based 2D Spatial Topology-Preserving Software Toolbox for Cloud/Cloud-Shadow Detection in Multi-Sensor Single-Date Earth Observation Multi-Spectral Imagery Eligible for Systematic ESA EO Level 2 Product Generation
Version 1
: Received: 16 August 2018 / Approved: 20 August 2018 / Online: 20 August 2018 (11:16:06 CEST)
Version 2
: Received: 27 August 2018 / Approved: 28 August 2018 / Online: 28 August 2018 (07:57:02 CEST)
Baraldi, A.; Tiede, D. AutoCloud+, a “Universal” Physical and Statistical Model-Based 2D Spatial Topology-Preserving Software for Cloud/Cloud–Shadow Detection in Multi-Sensor Single-Date Earth Observation Multi-Spectral Imagery—Part 1: Systematic ESA EO Level 2 Product Generation at the Ground Segment as Broad Context. ISPRS Int. J. Geo-Inf.2018, 7, 457.
Baraldi, A.; Tiede, D. AutoCloud+, a “Universal” Physical and Statistical Model-Based 2D Spatial Topology-Preserving Software for Cloud/Cloud–Shadow Detection in Multi-Sensor Single-Date Earth Observation Multi-Spectral Imagery—Part 1: Systematic ESA EO Level 2 Product Generation at the Ground Segment as Broad Context. ISPRS Int. J. Geo-Inf. 2018, 7, 457.
Baraldi, A.; Tiede, D. AutoCloud+, a “Universal” Physical and Statistical Model-Based 2D Spatial Topology-Preserving Software for Cloud/Cloud–Shadow Detection in Multi-Sensor Single-Date Earth Observation Multi-Spectral Imagery—Part 1: Systematic ESA EO Level 2 Product Generation at the Ground Segment as Broad Context. ISPRS Int. J. Geo-Inf.2018, 7, 457.
Baraldi, A.; Tiede, D. AutoCloud+, a “Universal” Physical and Statistical Model-Based 2D Spatial Topology-Preserving Software for Cloud/Cloud–Shadow Detection in Multi-Sensor Single-Date Earth Observation Multi-Spectral Imagery—Part 1: Systematic ESA EO Level 2 Product Generation at the Ground Segment as Broad Context. ISPRS Int. J. Geo-Inf. 2018, 7, 457.
Abstract
The European Space Agency (ESA) defines as Earth observation (EO) Level 2 information product a single-date multi-spectral (MS) image corrected for atmospheric, adjacency and topographic effects, stacked with its data-derived scene classification map (SCM), whose thematic map legend includes quality layers cloud and cloud-shadow. ESA EO Level 2 product generation is an inherently ill-posed computer vision (CV) problem never accomplished to date in operating mode by any EO data provider at the ground segment. Herein, it is considered: (I) necessary not sufficient pre-condition for the yet-unaccomplished dependent problems of semantic content-based image retrieval (SCBIR) and semantics-enabled information/knowledge discovery (SEIKD) in multi-source EO big data cubes. (II) Synonym of EO Analysis Ready Data (ARD) format. (III) Equivalent to a horizontal policy for background developments in Space Economy 4.0. In compliance with the GEO-CEOS Quality Assurance Framework for EO Calibration/Validation guidelines, to contribute toward filling an analytic and pragmatic information gap from multi-sensor EO big data to timely, comprehensive and operational EO value-adding information products and services, this work presents an innovative AutoCloud+ CV software toolbox for cloud and cloud-shadow quality layer detection in ESA EO Level 2 product. In vision, spatial information dominates color information. Inspired by this true-fact, the inherently ill-posed AutoCloud+ CV software was conditioned, designed and implemented to be “universal”, meaning fully automated (no human-machine interaction is required), near real-time, robust to changes in input data and scalable to changes in MS imaging sensor’s spatial and spectral resolution specifications.
Subject Areas
artificial intelligence; color naming; color constancy; cognitive science; computer vision; object-based image analysis (OBIA); physical and statistical data models; radiometric calibration; semantic content-based image retrieval; spatial topological and spatial non-topological information components
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Andrea Baraldi