Signal Processing 2019 DOI: 10.1016/j.sigpro.2019.05.005 View full text
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Jean-Baptiste Courbot, Vincent Mazet, Emmanuel Monfrini, Christophe Collet

Abstract: We consider the problem of segmentation in noisy, blurred astronomical hyperspectral images (HSI). Recent methods based on an hypothesis-testing framework handle the problem, but do not allow to use a prior on the result. This paper introduces a pairwise Markov field model, allowing the unsupervized Bayesian segmentation of faint sources in astronomical HSI. Results on synthetic images show that the segmentation methods outperform their state-of-the-art counterparts, and allow the detection at very low SNR. Be…

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