A Bayesian marked point process for object detection. Application to MUSE hyperspectral data

Florent Chatelain (1) , Aude Costard (2) , Olivier J.J. Michel (2)

1 GIPSA-SAIGA - GIPSA - Signal et Automatique pour la surveillance, le diagnostic et la biomécanique
2 GIPSA-CICS - GIPSA - Communication Information and Complex Systems

Marked point processes have received a great attention in the re- cent years, for their ability to extract objects in large data sets as those obtained in biological studies or hyperspectral remote sens- ing frameworks. This paper focuses on an original Bayesian point process estimation for the detection of galaxies from the hyperspec- tral data 'cubes' provided by the Multi Unit Spectroscopic Explorer (MUSE) instrument. It is shown that this approach allows to obtain a synthetic representation of the detection problem and circumvent the computational complexity inherent to high dimensional pixel based approaches. The reversible jump Monte Carlo Markov Chain imple- mented to sample the parameters is detailed, and the results obtained on benchmark data mimicking the real instrument are provided.

Mots clés: Point Processes; Hierarchical Bayesian Models; Hyperspectral Data; Galaxy Detection.; Galaxy Detection

Domaines: Traitement du signal et de l'image [eess.SP]; Traitement du signal et de l'image [eess.SP]

Florent Chatelain, Aude Costard, Olivier J.J. Michel. A Bayesian marked point process for object detection. Application to MUSE hyperspectral data. ICASSP 2011 - IEEE International Conference on Acoustics, Speech and Signal Processing, May 2011, Prague, Czech Republic. pp.3628-­3631. ⟨hal-00639711⟩