SAR image denoising via clustering based Linear Discriminant Analysis

Abstract

De-speckling is an important problem in synthetic aperture radar (SAR) image analysis. Speckle noise in SAR images is assumed to be representation of a multiplicative noise. Digital images plays an significant role in daily life applications such as satellite television, images of magnetic resonance as well as in the area of study such as remote sensing applications, geographic information system. The data set which are collected by the image sensor are sometimes polluted or infected by noise. The Synthetic Aperture Radar (SAR) is very useful for providing information about earth's surface by using the relative motion between antenna and its target. However, these images are affected with granular noise termed as Speckle noise. Speckle removal is one of many cause to give efficient image visualization, thus remains a major issue in SAR image processing. To address this problem here proposed the clustering based schema along with Linear Discriminant Analysis(LDA). The denoising of SAR image is based on clustering the noisy image into several disjoint local regions which has similar spatial structure and then again denoise each splitted regions by using wiener filtering in domain Linear Discriminant Analysis(LDA). The experimental resultant shows that the denoised patches of all clusters were finally used to reconstruct the noise-free image.