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RSS FeedsRemote Sensing, Vol. 11, Pages 1920: Combining ASNARO-2 XSAR HH and Sentinel-1 C-SAR VH/VV Polarization Data for Improved Crop Mapping (Remote Sensing)

 
 

17 august 2019 02:00:19

 
Remote Sensing, Vol. 11, Pages 1920: Combining ASNARO-2 XSAR HH and Sentinel-1 C-SAR VH/VV Polarization Data for Improved Crop Mapping (Remote Sensing)
 




The Advanced Satellite with New system ARchitecture for Observation-2 (ASNARO-2), which carries the X-band Synthetic Aperture Radar (XSAR), was launched on 17 January 2018 and is expected to be used to supplement data provided by larger satellites. Land cover classification is one of the most common applications of remote sensing, and the results provide a reliable resource for agricultural field management and estimating potential harvests. This paper describes the results of the first experiments in which ASNARO-2 XSAR data were applied for agricultural crop classification. In previous studies, Sentinel-1 C-SAR data have been widely utilized to identify crop types. Comparisons between ASNARO-2 XSAR and Sentinel-1 C-SAR using data obtained in June and August 2018 were conducted to identify five crop types (beans, beetroot, maize, potato, and winter wheat), and the combination of these data was also tested. To assess the potential for accurate crop classification, some radar vegetation indices were calculated from the backscattering coefficients for two dates. In addition, the potential of each type of SAR data was evaluated using four popular supervised learning models: Support vector machine (SVM), random forest (RF), multilayer feedforward neural network (FNN), and kernel-based extreme learning machine (KELM). The combination of ASNARO-2 XSAR and Sentinel-1 C-SAR data was effective, and overall classification accuracies of 85.4 ± 1.8% were achieved using SVM.


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36 viewsCategory: Geology, Physics
 
Remote Sensing, Vol. 11, Pages 1915: An Analysis of Ground-Point Classifiers for Terrestrial LiDAR (Remote Sensing)
Remote Sensing, Vol. 11, Pages 1919: Comparison of LiDAR and Digital Aerial Photogrammetry for Characterizing Canopy Openings in the Boreal Forest of Northern Alberta (Remote Sensing)
 
 
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