An estimator for compound-Gaussian multilook SAR clutter amplitude with inverse gamma texture

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 log-cumulants (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.