SAR Model Based Regularization Methods for Image Texture Classification

Image texture classification and segmentation is a main topic in the analysis of many types of images. People usually use the least squares estimation (LSE) for analyzing SAR textures. But we find that the LSE is unstable in practical computation. Therefore, in this paper we present regularization methods for image texture classification and segmentation. Regularization is such a technique which can successfully suppress the instability due to noise or truncation error when computing. Several regularization techniques, including standard regularization (SR), penalized regularization (PR) and total variation based regularization (TVR), are exhibited to reduce instability in texture extraction. Experiment results demonstrate that the regularization methods are superior to LSE and seem to be promising in practical applications.