Speckle reduction of SAR images using support vector machine in wavelet domain

Abstract

The granular appearance of speckle noise in synthetic aperture radar (SAR) imagery makes it very difficult to visually and automatically interpret SAR data. Therefore, speckle reduction is a prerequisite for many SAR image processing tasks. We develop a speckle reduction algorithm by fusing the wavelet denoising technique with support vector machine (SVM). Based on the least squares support vector machine (LS-SVM) with Gaussian radial basis function kernel, a new denoising operators used in the wavelet domain are obtained. Simulated SAR images and real SAR images are used to evaluate the denoising performance of our proposed algorithm along with another wavelet-based denoising algorithm, as well as the refined Lee speckle filter. Experimental results show that the that the proposed filter method outperforms standard wavelet denoising techniques in terms of the ratio images and the equivalent-number-of-looks measures in most cases. It also achieves better performance than the refined Lee filter.