The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLIII-B3-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2020, 275–279, 2020
https://doi.org/10.5194/isprs-archives-XLIII-B3-2020-275-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2020, 275–279, 2020
https://doi.org/10.5194/isprs-archives-XLIII-B3-2020-275-2020

  21 Aug 2020

21 Aug 2020

TEXTURE ANALYSIS FOR LAND USE LAND COVER (LULC) CLASSIFICATION IN PARTS OF AHMEDABAD, GUJARAT

V. Nizalapur and A. Vyas V. Nizalapur and A. Vyas
  • Centre for Applied Geomatics, CRDF, CEPT University, Navrangpura, Ahmedabad, Gujarat, India

Keywords: SAR, RADARSAT-2, Grey Level Co-occurrence, Texture, Principal Component, Land Use Land Cover

Abstract. The present study addresses the potential of RADARSAT-2 data for Land Use Land Cover (LULC) Classification in parts of Ahmedabad, Gujarat, India. Texture measures of the original SAR data were obtained by the Gray Level Co-occurrence Matrix (GLCM). Results suggested False Colour Composite (FCC) of Mean, Homogeneity and Entropy showed a good discrimination of different land cover classes. Further, Principal Component Analysis (PCA) was also applied to the eight texture measures and FCC of Principal components is generated. Unsupervised classification is carried out for the above generated FCCs and accuracy assessment is carried out. The result of classification shows that the PCA generated from GLCM texture measures could obtain higher accuracy than using only the classification carried out by texture measures. Overall results of the study suggested possible use of single polarization and single date Radarsat-2 data for LULC classification with better accuracy using PCA generated image.