Detecting Changes in Fully Polarimetric SAR Imagery with Statistical Information Theory

TL;DR

This paper compares classical and information-theoretic statistical methods for detecting changes in polarimetric SAR images, demonstrating that entropy-based tests often outperform traditional likelihood ratio approaches.

Contribution

It introduces a comparison of change detection methods using the scaled complex Wishart distribution, highlighting the effectiveness of entropy-based measures over classical likelihood ratio tests.

Findings

Entropy-based tests outperform likelihood ratio in simulated data

Entropy measures show higher detection power in real PolSAR data

Statistical methods effectively identify changes in complex covariance matrices

Abstract

Images obtained from coherent illumination processes are contaminated with speckle. A prominent example of such imagery systems is the polarimetric synthetic aperture radar (PolSAR). For such remote sensing tool the speckle interference pattern appears in the form of a positive definite Hermitian matrix, which requires specialized models and makes change detection a hard task. The scaled complex Wishart distribution is a widely used model for PolSAR images. Such distribution is defined by two parameters: the number of looks and the complex covariance matrix. The last parameter contains all the necessary information to characterize the backscattered data and, thus, identifying changes in a sequence of images can be formulated as a problem of verifying whether the complex covariance matrices differ at two or more takes. This paper proposes a comparison between a classical change detection method based on the likelihood ratio and three statistical methods that depend on information-theoretic measures: the Kullback-Leibler distance and two entropies. The performance of these four tests was quantified in terms of their sample test powers and sizes using simulated data. The tests are then applied to actual PolSAR data. The results provide evidence that tests based on entropies may outperform those based on the Kullback-Leibler distance and likelihood ratio statistics.