A Neural Network Approach to Classify Mixed Classes Using Multi Frequency Sar data

Abstract

Classification of mixed classes that are having similar backscatter response at different polarization combinations, using single frequency synthetic aperture radar (SAR) data is very intricate and there is always a high possibility of misclassification. Therefore, the main objective of this study is to classify the mixed classes using multi-frequency SAR data. An artificial neural network (ANN) approach is used for classification of the considered mixed classes using various polarimetric parameters obtained from single acquisition ALOS2 PALSAR (L band) and Sentinel 1 (C band) dual pol SAR data. An image statistical measure based separability index analysis is used to identify the optimal polarimetric parameters for developing the classifier. It is observed that, the proposed multi-frequency approach is able to classify the mixed classes with an overall accuracy of 87%.