Abstract
Ground cover classification is one of the core applications in remote sensing. Classification of imaging radar data using conventional single-channel or dual-channel usually results in poor accuracy due to limited number of observations. For this reason, applications of full polarimetric data are growing. In this article, Radarsat-2 quad-pol data of an agriculture scene acquired on August, 2013 over the Nile Delta, Egypt, has been used to evaluate the accuracy of three supervised classification methods, Support Vector Machine (SVM), multilayer perceptron (MLP), and decision tree (DT), in discriminating between four different crops: rice, maize, grape and cotton, in addition to urban land cover. A set of principal components was used in the SVM classifier. Our goal is to build a generic model that minimizes the error of future classification using the same training data. Results indicate that combining the most commonly used polarimetric parameters from Pauli, Cloude-Pottier, and Freeman-Durden decompositions along with the three fundamental backscatter coefficients (sigma(0)(hh), sigma(0)(vv), and sigma(0)(hv)) using SVM classifier strike an appropriate balance that minimizes the effect of the training error on the classification accuracy without going to over-fitting classification. This leads to the highest overall classification accuracy of 94.48%. The overall classification accuracy using different combinations of parameters and different classifiers is given.