The Effect of Hybrid Polarimetric Descriptors on Classification Accuracy of Various Land Cover Types

Varsha Turkar, Shaunak De, Anup Das, Sanjay Shitole, Rinki Deo, Kaushik Patnaik

Abstract: RISAT-1 data is acquired over Mumbai in hybrid and linear dual polarizations. The mean and standard deviation of backscattering coefficients ( σ <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sup> ) are computed and have been analyzed for various land features. Classification accuracy between RISAT-1 hybrid and dual polarimetric data has been compared. The effect of different multilook on the classification accuracy is also studied. Wishart supervised and Support Vector Machine (SVM) classifiers are used for this study. It has been observed that the classification accuracy can be improved by using m-δ or m-χ decomposition along with Circular Polarization Ratio (CPR) and SPAN of hybrid polarimetric data.