Region-Based Classification of SAR Images Using Kullback-Leibler Distance Between Generalized Gamma Distributions

For the classification of synthetic aperture radar (SAR) images, traditional pixel-based Bayesian classifiers suffer from an intrinsic flaw that categories with serious overlapped probability density functions cannot be well classified. To solve this problem, in this letter, a region-based classifier for SAR images is proposed, where regions, instead of individual pixels, are treated as elements for classification. In the algorithm, each region is assigned to the class that minimizes a criterion referring to the Kullback-Leibler distance. Besides, the generalized gamma distribution (G Gamma D), a flexible empirical model, is employed for the statistical modeling of SAR images. Finally, with a synthetic image and an actual SAR image acquired by the EMISAR system, the effectiveness of the proposed algorithm is validated, compared with the pixel-based maximum-likelihood method and two region-based Bayesian classifiers.