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

Abstract

To cite this version:Céline Meillier, Florent Chatelain, Olivier Michel, Hacheme Ayasso. Non-parametric Bayesian framework for detection of object configurations with large intensity dynamics in highly noisy hyperspectral data. IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2014), May 2014, Florence, Italy. Proceedings of ICASSP 2014, pp.1905-1909, 2014 ABSTRACTIn 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.

Nonparametric Bayesian Extraction of Object Configurations in Massive Data

“…3) Global Prior Density of the Process: The density of the process with respect to the normalized reference Poisson process including now becomes (18) where is the point process density defined in (14). Note that other penalization terms might also be introduced to take into account the knowledge that we have about the configuration of the objects to be detected.…”

“…This present paper extends and gives extensive details on the method summarized in [18]. All steps in the algorithm are discussed and carefully introduced.…”

Detection of faint extended sources in hyperspectral data and application to HDF-S MUSE observations

“…To our knowledge, the problem of detecting a faint extended source in multispectral or hyperspectral images has not been previously handled: a very common aspect of source detection in HSI is the search for punctual or quasi-punctual objects [4,5,8,9]. This is mainly because most extended sources are not faint in remote sensing and are addressed in a classification context, while instruments in other domains do not provide sufficient spatial or spectral resolutions.…”