A Classification Method of Polarimetric SAR Data based on Scattering Models and Neural Networks

Abstract

We consider a polarimetric SAR data classification method which uses scattering models. The proposed method is a neural network classifier composed of two classification procedures. First, SAR data is pre-classified into three scattering classes (odd, even, and diffuse & unable to classify) by individually computing the Mueller matrix and Stokes vector based on the van Zyl approach. Second, a neural network which suits to each scattering class is constructed and the data is identified as a final land cover type. Either the competitive or back-propagation neural network can be employed as a classifier. It is possible to identify more detail category in order to analyze scattering classes. As a result of the procedure using SIR-C C band data, 11 land cover types by combination of five categories and three scattering classes are identified. We assume the effectiveness of the proposed method in comparison to classification by checking the ground truth data.