A Robust Change Detector for Highly Heterogeneous Multivariate Images

Abstract

In this paper, we propose new detectors for Change Detection between two multivariate images. The data is supposed to fol-Iowa 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.

Publication
In 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Ammar Mian
Ammar Mian
Associate professor

Associate professor at Université Savoie Mont Blanc in Signal processing

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