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Spectral-spatial classification of polarimetric SAR data using morphological attribute profiles

Morphological profiles (MPs) have been effective tools to fuse spectral and spatial information for the classification of remote sensing data. However, the previous applications have been limited to the multi-/ hyper-spectral data analysis. In this study, the application of morphological profiles is extended for the classification of polarimetric synthetic aperture radar (POLSAR) data. The MPs are constructed with the diagonal elements of the covariance matrix and the features derived from the eigenvalue decomposition method. The resulting extended morphological profile (EMP) which is a stack of all the MPs of various features is used for supervised classification of the images using a support vector machine (SVM) classifier. It is shown that significant improvements in classification accuracies can be achieved by using the profiles.