The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLVI-4/W2-2021
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVI-4/W2-2021, 133–138, 2021
https://doi.org/10.5194/isprs-archives-XLVI-4-W2-2021-133-2021
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVI-4/W2-2021, 133–138, 2021
https://doi.org/10.5194/isprs-archives-XLVI-4-W2-2021-133-2021

  19 Aug 2021

19 Aug 2021

LAND-COVER CLASSIFICATION USING FREELY AVAILABLE MULTITEMPORAL SAR DATA (WORK IN PROGRESS)

M. Rajngewerc1, R. Grimson1, J. L. Bali2, P. Minotti1, and P. Kandus1 M. Rajngewerc et al.
  • 1Instituto de Investigación e Ingeniería Ambiental, Universidad Nacional de San Martín, CONICET, 3iA, Buenos Aires, Argentina
  • 2YTEC, YPF-CONICET, Buenos Aires, Argentina

Keywords: SAR, Sentinel-1, GLCM, textures, classification, wetlands

Abstract. Synthetic Aperture Radar (SAR) images are a valuable tool for wetlands monitoring since they are able to detect water below the vegetation. Furthermore, SAR images can be acquired regardless of the weather conditions. The monitoring and study of wetlands have become increasingly important due to the social and ecological benefits they provide and the constant pressures they are subject to. The Sentinel-1 mission from the European Space Agency enables the possibility of having free access to multitemporal SAR data. This study aims to investigate the use of multitemporal Sentinel-1 data for wetlands land-cover classification. To perform this assessment, we acquired 76 Sentinel-1 images from a portion of the Lower Delta of the Paraná River, and considering different seasons, texture measurements, and polarization, 30 datasets were created. For each dataset, a Random Forest classifier was trained. Our experiments show that datasets that included the winter dates achieved kappa index values (κ) higher than 0.8. Including textures measurements showed improvements in the classifications: for the summer datasets, the κ increased more than 14%, whereas, for Winter datasets in the VH and Dual polarization, the improvements were lower than 4%. Our results suggest that for the analyzed land-cover classes, winter is the most informative season. Moreover, for Summer datasets, the textures measurements provide complementary information.