SAR image classification based on alpha-stable distribution

Abstract

We propose a classification algorithm that utilizes the alpha-stable distribution to model the texture features of synthetic aperture radar (SAR) images. The SAR image is first decomposed by stationary wavelet transform (SWT). After that, the alpha-stable distribution is applied to model the high-frequency subband coefficients of the image at each decomposition scale. A regression-type method is then used to estimate the alpha-stable distribution parameters, which form a feature vector that fully describes the texture. Finally, a SAR image classification algorithm is derived by exploiting this feature vector based on the support vector machines (SVM) approach. Because different combinations of alpha-stable distribution parameters contribute to differences in classification precision, a multilevel SVM (MSVM) classification algorithm is also presented to address the issue. Experimental results indicate that the proposed SAR image classification algorithm is effective and the MSVM algorithm improves the classification performance. Moreover, our proposed algorithm has low computational cost as only a small number of the alpha-stable distribution parameters are processed.