Texture Estimation by Despeckling of SAR Imagery

Abstract

Synthetic Aperture Radar (SAR) is a powerful signal processing system that finds its application in diverse fields. This system achieves finer resolution in both range and azimuth. However, coherent nature of the same makes it susceptible to signal dependent, multiplicative noise known as speckle. It’s granularity is responsible for degradation of radiometric resolution which in turn makes visual interpretation a difficult task. Furthermore, presence of such noise degrades accuracy of detector, classifier designed for SAR clutter data. This entitles the development of SAR despeckling filters, and has become a major research area over past few years. In this research work, SAR clutter data is modeled using multiplicative models such as K, W and CR-CG distributions which gives prior knowledge regarding texture and amplitude speckle statistics. This knowledge is further utilized to formulate Γ-MAP, β-MAP, CR-MAP estimate of texture amplitude which in turn is helpful for designing MAP based despeckling filters. Furthermore, estimators for the parameters associated with texture estimate using MoLC and EM algorithm are formulated. Few MMSE based despeckling filters such as Lee, Kuan are also analyzed for extracting texture from clutter data. Experiments are carried out on 1-look real SAR clutter data and 3-look synthetic data to examine the suitability and applicability of these filters in despeckling areas of diverge kind. Furthermore, effectiveness of the same is assessed in terms of mean preservation and speckle suppression using recently proposed statistical measures, MPI and MPSSI. Experimental results clearly illustrates that CR-MAP filter outperforms state-of-the-art filters in preserving mean while effectively suppressing speckle for SAR clutter data with varying degree of roughness.