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

Abstract

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.

Acknowledgement

This work was supported in part by the National Natural Science Foundation of China (40271077), by the National Important Fundamental Research Plan of China (2001CB309401), by the Research Fund for the Doctoral Program of Higher Education of China, and by the Fundamental Research Foundation of Tsinghua University.