Classification Accuracy of Multi-Frequency and Multi-Polarization SAR Images for Various Land Covers

Abstract

This paper presents the land cover classification capabilities of fully versus partially polarimetric SAR data for C- and L-band frequencies. Maximum Likelihood classifier with complex Wishart distribution and artificial neural network classifier (ANN) have been used for classification. The change in accuracy due to the phase information of SAR data is also assessed by comparing the classified results of intensity and complex images for all the possible polarization combinations at L- and C-band. In all the combinations, fully polarimetric data provides highest accuracy and it is not much different from that of complex partial polarimetric (HH, VV) combination. The accuracies obtained with various partial polarimetric combinations are dependent on the land cover types. Among L-, C- and X-bands, L-band offers better accuracy. By combining all bands data, accuracy improved by 7%.The accuracy has been improved slightly by combining the three components of van Zyl decomposition with the combination of X-, C-and L-band. IRS-P6 optical data over the same area has been used to compare the classification accuracy between optical and SAR data.