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ICASSP
2011
IEEE

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

9 years 5 months ago
A Bayesian marked point process for object detection. Application to muse hyperspectral data
Marked point processes have received a great attention in the recent years, for their ability to extract objects in large data sets as those obtained in biological studies or hyperspectral remote sensing frameworks. This paper focuses on an original Bayesian point process estimation for the detection of galaxies from the hyperspectral 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 implemented to sample the parameters is detailed, and the results obtained on benchmark data mimicking the real instrument are provided.
Florent Chatelain, Aude Costard, Olivier J. J. Mic
Added 21 Aug 2011
Updated 21 Aug 2011
Type Journal
Year 2011
Where ICASSP
Authors Florent Chatelain, Aude Costard, Olivier J. J. Michel
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