Non-parametric Bayesian framework for detection of object configurations with large intensity dynamics in highly noisy hyperspectral data - Archive ouverte HAL Access content directly
Conference Papers Year : 2014

Non-parametric Bayesian framework for detection of object configurations with large intensity dynamics in highly noisy hyperspectral data

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Abstract

In this study, a method that aims at detecting small and faint objects in noisy hyperspectral astrophysical images is presented. The particularity of the hyperspectral images that we are interested in is the high dynamics between object intensities. Detection of the smallest and faintest objects is challenging, because their signal-to-noise ratio is low, and if the brightest objects are not well reconstructed, their residuals can be more energetic than faint objects. This paper proposes a marked point process within a nonparametric Bayesian framework for the detection of galaxies in hyperspectral data. The efficiency of the method is demonstrated on synthetic images, and it provides good results for very faint objects in quasi-real astrophysical hyperspectral data.
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Dates and versions

hal-00991388 , version 1 (15-05-2014)

Identifiers

  • HAL Id : hal-00991388 , version 1

Cite

Céline Meillier, Florent Chatelain, Olivier J.J. Michel, H Ayasso. Non-parametric Bayesian framework for detection of object configurations with large intensity dynamics in highly noisy hyperspectral data. ICASSP 2014 - IEEE International Conference on Acoustics, Speech and Signal Processing, May 2014, Florence, Italy. pp.1905-1909. ⟨hal-00991388⟩
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