A Review on Parametric and Semiparametric Distributions in Characterizing Synthetic Aperture Radar Clutter Data

Clutter modeling is used in diverse Synthetic Aperture Radar (SAR) image processing fields, e.g., speckle suppression, detection, classification, and recognition. Therefore, accurate formulation of SAR clutter models is crucial to achieve effective performance in such applications. We present a survey on parametric and semi-parametric distributions in characterizing SAR ground clutter statistics, and we compare estimators for their parameters (when explicitly available) with a theoretical assessment of their computational burden. Furthermore, we discuss how to assess these models with the
$\widetilde {k}_{3} \sim \widetilde {k}_{2}$
diagram. We also discuss how to analyze the homogeneity of SAR clutter data using clutter models’ coefficient of variation (
$C_{v}$
) to justify their effectiveness in portraying scene heterogeneity. Experiments are made on simulated data and SAR images to assess the goodness-of-fit, deviation of estimated
$C_{v}$
from the observed
$C_{v}$
, and computational complexity for the state-of-the-art clutter models, thereby assessing their effectiveness in characterizing SAR clutter amplitude statistics. The MATLAB code that implements these tools is available at
https://github.com/dkmahapatra1/SAR-Clutter-Modelling.git