A Robust Change Detector for Highly Heterogeneous Multivariate Images

In this paper, we propose new detectors for Change Detection between two multivariate images. The data is supposed to follow a Compound Gaussian distribution. By using Likelihood Ratio Test (LRT) and Generalised LRT (GLRT) approaches, we derive our detectors. The CFAR behaviour has been studied and the simulations show that they outperform the classic Gaussian Detector when the data is highly heterogeneous.

Mots clés

Domaines

Traitement du signal et de l'image [eess.SP]

Fichier principal

ChangeDetectionSIRVICCASP.pdf (991.72 Ko)

Origine	Fichiers produits par l'(les) auteur(s)
Licence	Autorisation HAL

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https://hal.univ-grenoble-alpes.fr/hal-01840783

Soumis le : lundi 16 juillet 2018-16:30:35

Dernière modification le : samedi 27 septembre 2025-19:08:42

Archivage à long terme le : mercredi 17 octobre 2018-16:26:26

Dates et versions

hal-01840783 , version 1 (16-07-2018)

Licence

Autorisation HAL

Identifiants

HAL Id : hal-01840783 , version 1
DOI : 10.1109/icassp.2018.8462253

Citer

Ammar Mian, Jean-Philippe Ovarlez, Guillaume Ginolhac, Abdourahmane M Atto. A Robust Change Detector for Highly Heterogeneous Multivariate Images. 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, Apr 2018, Calgary, Canada. ⟨10.1109/icassp.2018.8462253⟩. ⟨hal-01840783⟩