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A new clustering algorithm for PolSAR images segmentation

Abstract : This paper deals with polarimetric synthetic aperture radar (PolSAR) image segmentation. More precisely, we present a new robust clustering algorithm designed for non-Gaussian data. The algorithm is based on an expectation-maximization approach. Its novelty is that, in addition to the estimation of each cluster center and covariance matrix, it also provides for each observation an estimation of the scale parameter , allowing a better flexibility when assigning each observation in one cluster. The method performances are evaluated on both simulated and real multi-looked PolSAR data. It is demonstrated that the algorithm outperforms the classical clustering algorithms such as k-means and GMM (Gaussian-based EM algorithm) in various scenarios.
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Contributor : Frédéric Pascal Connect in order to contact the contributor
Submitted on : Thursday, February 20, 2020 - 2:33:25 PM
Last modification on : Sunday, June 26, 2022 - 2:27:56 AM
Long-term archiving on: : Thursday, May 21, 2020 - 4:02:44 PM


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  • HAL Id : hal-02485766, version 1


Violeta Roizman, Gordana Draskovic, Frédéric Pascal. A new clustering algorithm for PolSAR images segmentation. IEEE CAMSAP 2019 - IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, Dec 2019, Guadeloupe, West Indies, France. ⟨hal-02485766⟩



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