Version 1
: Received: 21 November 2017 / Approved: 22 November 2017 / Online: 22 November 2017 (05:37:02 CET)
How to cite:
Mancini, A.; Dyson, J.; Frontoni, E.; Zingaretti, P. Soil and Crop/Tree Segmentation from Remotely Sensed Data by Using Digital Surface Models. Preprints2017, 2017110142. https://doi.org/10.20944/preprints201711.0142.v1
Mancini, A.; Dyson, J.; Frontoni, E.; Zingaretti, P. Soil and Crop/Tree Segmentation from Remotely Sensed Data by Using Digital Surface Models. Preprints 2017, 2017110142. https://doi.org/10.20944/preprints201711.0142.v1
Mancini, A.; Dyson, J.; Frontoni, E.; Zingaretti, P. Soil and Crop/Tree Segmentation from Remotely Sensed Data by Using Digital Surface Models. Preprints2017, 2017110142. https://doi.org/10.20944/preprints201711.0142.v1
APA Style
Mancini, A., Dyson, J., Frontoni, E., & Zingaretti, P. (2017). Soil and Crop/Tree Segmentation from Remotely Sensed Data by Using Digital Surface Models. Preprints. https://doi.org/10.20944/preprints201711.0142.v1
Chicago/Turabian Style
Mancini, A., Emanuele Frontoni and Primo Zingaretti. 2017 "Soil and Crop/Tree Segmentation from Remotely Sensed Data by Using Digital Surface Models" Preprints. https://doi.org/10.20944/preprints201711.0142.v1
Abstract
The increased availability of high resolution remote sensor data for precision agriculture 1 applications permits users to aquire deeper and more relevant knowledge about crops states that lead 2 inevitably to better decisions. The algorithm libraries being developed and evolved around these 3 applications rely on multi-spectral or hyper-spectral data acquired by using manned or unmanned 4 platforms. The current state of the art makes thorough use of vegetational indicies to guide the 5 operational management of agricultural land plots. One of the most challenging sub-problems is 6 to correctly identify and separate crop from soil. Thresholding techniques based on Normalized 7 Difference Vegetation Index (NDVI) or other such similar metrics have the advantage of being simple, 8 easy to read transformations of the data packed with useful information. Obvious difficulties arise 9 when crop/tree and soil have similar spectral responses as in case of grass filled areas in vineyards. 10 In this case grass and canopy are close in terms of NDVI values and thresholding techniques will 11 generally fail. Radiometric approaches could be integrated or replaced by a geometric approach that 12 is based on terrain data like Digital Surface Models (DSMs). These models are one of the ouputs 13 of orthorectification engines usually present in data acquired by using unmanned platforms. In 14 this paper we present two approaches based on DSM that are able to segment crop/tree from soil 15 while over gradient terrain. The DSM data are processed through a two dimensional data slicing or 16 reduction technique. Each slice is separately processed as a one dimensional time series to derive the 17 terrain and tree structures separately, here interpreted as object probability densities. In particular 18 the first approach is a Cartesian grid rasterization (CARSCAN) of the terrain and the second is its 19 immediate generalisation or radial grid rasterization of the DSM model (FANSCAN). The FANSCAN 20 recovers information from the original image at greater frequencies on the Fourier plane. These 21 approaches enable the identification of crop/tree from soil in case of slopes or hilly terrain without 22 any constraint on the displacement / direction of plant/tree row. The proposed algorithm uses pure 23 DSM information even if it is possible to fuse its output with other classifiers.
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.
