Sampling Spatially Correlated Clutter

TL;DR

This paper presents a method to model and simulate spatially correlated clutter in coherent imaging systems using correlated ${ m G}_A^0$ distributions, enabling more accurate representation of shadowing effects in SAR images.

Contribution

It introduces a general approach for simulating correlated ${ m G}_A^0$-distributed fields using the Inverse Transform method applied to Gaussian fields, allowing negative correlations.

Findings

Effective simulation of correlated ${ m G}_A^0$ fields with negative correlation.

Improved modeling of shadowing effects in SAR imagery.

Enhanced understanding of clutter in coherent imaging systems.

Abstract

Correlated ${\cal G}$ distributions can be used to describe the clutter seen in images obtained with coherent illumination, as is the case of B-scan ultrasound, laser, sonar and synthetic aperture radar (SAR) imagery. These distributions are derived using the square root of the generalized inverse Gaussian distribution for the amplitude backscatter within the multiplicative model. A two-parameters particular case of the amplitude ${\mathcal G}$ distribution, called ${\mathcal G}_{A}^{0}$, constitutes a modeling improvement with respect to the widespread ${\mathcal K}_{A}$ distribution when fitting urban, forested and deforested areas in remote sensing data. This article deals with the modeling and the simulation of correlated ${\mathcal G}_{A}^{0}$-distributed random fields. It is accomplished by means of the Inverse Transform method, applied to Gaussian random fields with spatial correlation. The main feature of this approach is its generality, since it allows the introduction of negative correlation values in the resulting process, necessary for the proper explanation of the shadowing effect in many SAR images.