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Communication Dans Un Congrès Année : 2018

A Robust Change Detector for Highly Heterogeneous Multivariate Images

Résumé

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.
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Dates et versions

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

Identifiants

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⟩
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