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.

Abstract. This paper aims to discusses the extraction of urban features from airborne NISAR (NASA-ISRO SAR) data using deep learning algorithm for a part of Ahmedabad City. NISAR data is acquired in two wavelength bands (L and S) in hybrid polarization i.e., RH and RV. This study has used level two data viz., amplitude data. Pre-processing of NISAR data in L and S wavelength bands was carried out by using MIDAS, software developed and provided by the Space Applications Centre. Pre-processing viz., Speckle suppression using different filters in varying window sizes, radiometric and geometric calibration was performed. Variation of backscattering coefficient (Sigma- nought) in different wavelengths and polarizations for different land use features were analysed. NISAR data in conjunction with LISS 4 (5.8 m resolution) data is subjected to different fusion techniques. Qualitative and Quantitative analysis was carried out and Gram Schmidt technique was chosen for further analysis. Segmentation was performed to achieve better analysis of the fused image and the amplitude image. Lastly, a deep learning architecture was developed for the automatic classification of the image, and the Convolution Neural Network model was designed using mobile net and the regularization techniques. Deep learning architecture in conjunction with e-cognition developer was used for extracting urban features.

Abstract. The present study addresses the potential of airborne NASA – ISRO Synthetic Aperture Radar (NISAR) compact polarimetric (CP) data to discriminate the land cover classes emphasizing the urban area for parts of Ahmedabad city, India. This has been carried out by generating m-Delta, m-Chi and m-Alpha polarimetric decompositions using Compact Polarimetric L band NISAR data. In Hybrid Polarimetric data, both m-delta and m-chi decompositions have almost the same formulations, indicating that delta and chi play the same roles as indicators of single-bounce and double-bounce scattering. However, M-delta seem preferable over M-chi as stoke parameter delta is highly susceptible towards orientation. It is also observed that building orientation and density has effect on scattering pattern. This is attributed to the target orientation which is parallel to the look direction of the sensor. Supervised classification of m-Delta decomposition was carried out and over all accuracy of 81.1 % was observed in the study.