A generalized gamma copula model for high resolution polarimetric SAR change detection

Stephen Herman; Joshua Ash

doi:10.1117/12.3028781

7 June 2024 A generalized gamma copula model for high resolution polarimetric SAR change detection

Stephen Herman, Joshua Ash

Proceedings Volume 13032, Algorithms for Synthetic Aperture Radar Imagery XXXI; 130320D (2024) https://doi.org/10.1117/12.3028781
Event: SPIE Defense + Commercial Sensing, 2024, National Harbor, Maryland, United States

Abstract

In this paper, we describe a new approach to non-coherent change detection for high resolution polarimetric synthetic aperture radar (polSAR) exploitation. In the high resolution setting, the reduced size of a resolution cell diminishes the applicability of central limit theorem arguments that lead to the traditional Gaussian backscatter models that underpin existing polSAR change detection algorithms. To mitigate this, we introduce a new model for polSAR data that combines generalized Gamma (GΓ) distributed marginals within a copula framework to capture the correlation dependency between multiple polSAR channels. Using the GΓ-copula model, a generalized likelihood ratio test (GLRT) is derived for detecting changes within high resolution polSAR imagery. Examples using measured data demonstrate the non-Gaussian nature of high resolution polSAR data and quantify a performance improvement when using the proposed GΓ-copula change detection framework.

Conference Presentation

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Stephen Herman and Joshua Ash "A generalized gamma copula model for high resolution polarimetric SAR change detection", Proc. SPIE 13032, Algorithms for Synthetic Aperture Radar Imagery XXXI, 130320D (7 June 2024); https://doi.org/10.1117/12.3028781

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