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Pairwise Markov fields for segmentation in astronomical hyperspectral images

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. Besides, results on real images provide encouraging detections with respect to the application.
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Preprints, Working Papers, ...
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Contributor : Jean-Baptiste Courbot Connect in order to contact the contributor
Submitted on : Thursday, February 7, 2019 - 11:05:14 AM
Last modification on : Thursday, December 2, 2021 - 3:17:03 AM
Long-term archiving on: : Wednesday, May 8, 2019 - 1:26:11 PM


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  • HAL Id : hal-02010449, version 1


Jean-Baptiste Courbot, Vincent Mazet, Emmanuel Monfrini, Christophe Collet. Pairwise Markov fields for segmentation in astronomical hyperspectral images. 2019. ⟨hal-02010449⟩



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