Polarimetric SAR image classification by using generalized optimization of polarimetric contrast enhancement

In this letter, a generalized optimization of polarimetric contrast enhancement (GOPCE) is employed for supervised polarimetric synthetic aperture radar (SAR) image classification. The GOPCE is the extension of optimization of polarimetric contrast enhancement (OPCE), and it includes three optimal coefficients associated with the Cloude entropy and two special similarity parameters in addition to the optimal polarization states. Using the GOPCE, the authors propose an approach to supervised classification. For comparison, the authors also use the maximum likelihood (ML) classifier for classification, based on the complex Wishart distribution. The classification results of a NASA/JPL AIRSAR L-band image over San Francisco demonstrate the effectiveness of the proposed approach.