Classification of PolSAR data with the Complex Riesz distribution

This article deals with unsupervised classification strategies applied to polarimetric synthetic aperture radar (PolSAR) images. We discuss the performance of the Complex Riesz distribution, which is used for the first time to classify PolSAR images. Hence, we extend the maximum likelihood (ML) and...

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Published in:Communications in statistics. Theory and methods Vol. 54; no. 19; pp. 6191 - 6218
Main Authors: Kammoun, Rayhan, Kessentini, Sameh, Zine, Raoudha
Format: Journal Article
Language:English
Published: Philadelphia Taylor & Francis 02.10.2025
Taylor & Francis Ltd
Subjects:
Algorithms
Classification
Complex Riesz distribution
expectation-maximization algorithm
Hermitian random matrix
maximum likelihood algorithm
PolSAR classification
Radar imaging
stochastic distances
Synthetic aperture radar
Synthetic data
ISSN:0361-0926, 1532-415X
Online Access:Get full text
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Summary:This article deals with unsupervised classification strategies applied to polarimetric synthetic aperture radar (PolSAR) images. We discuss the performance of the Complex Riesz distribution, which is used for the first time to classify PolSAR images. Hence, we extend the maximum likelihood (ML) and expectation-maximization (EM) algorithms to the Complex Riesz distribution. Furthermore, we derive the analytic expression of five stochastic distances (Kullback-Leibler, Bhattacharyya, Rényi, Hellinger, and Chi-square) between Complex Riesz distributions. We assess the accuracy of the Complex Riesz EM algorithm on synthetic data generated by an extension of the Bartlett decomposition. Then, comparing the Complex Wishart and the Complex Riesz distributions on PolSAR data reveals that the latter performs better than the former. Finally, the EM algorithm for the Complex Riesz distribution serves to classify the actual data, and the discrimination potential of the five stochastic distances is discussed. These results in both experimental, ML, and EM algorithms suggest that most stochastic distances are significant, and mainly the 0. 7-order Rényi.
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ISSN:0361-0926
1532-415X
DOI:10.1080/03610926.2025.2450775