An Estimator for Compound-Gaussian Multilook SAR Clutter Amplitude with Inverse Gamma Texture

Abstract

In the context of synthetic aperture radar (SAR) data analysis, formulation of accurate models for clutter statistics is a crucial task. In this paper, compound-Gaussian distribution with inverse gamma texture (IΓ-CG), is presented for multilook SAR amplitude data. An estimator based on method of logcumulants (MoLC), which stems from the adoption of second kind statistics and Mellin transform is developed for estimating its parameters. The IΓ-CG model is validated by using multilook synthetic data and single look real clutter data of amplitude SAR images. Experimental results show that the IΓ-CG model outmatches the state-of-the-art pdfs that clearly demonstrates the applicability of the model.