NDLI: Separation of stellar spectra from hyperspectral images using particle filtering constrained by a parametric spatial mixing model

Content Provider	IEEE Xplore Digital Library
Author	Selloum, A. Deville, Y. Carfantan, H.
Copyright Year	2013
Description	Author affiliation: IRAP, Univ. de Toulouse, Toulouse, France (Selloum, A.; Deville, Y.; Carfantan, H.)
Abstract	In a hyperspectral image of a dense stellar field, each pixel is a mixture of contributions from the star spectra. Indeed, because of the data acquisition system, each star spectrum is spread out over several pixels, which is modeled by the PSF (point spread function). The objective of our work is to develop a method to separate star spectra. The star spectra can be highly correlated and are not sparse. Therefore, the classical blind source separation (BSS) methods based on Independent Component Analysis (ICA) or spectral sparsity are not appropriate to solve this problem. On the other hand, methods based on Non-negative Matrix Factorisation (NMF) are sensitive to the initialization and can't account for a particular structure of the mixing matrix. In this paper, we propose to solve this problem with a Sequential Bayesian method (particle filter). This method is based on a hidden Markov model in which we take into account the particular structure of the mixing matrix (PSF model), prior information about the PSF parameters and star positions but do not use prior information concerning the spectra. The results obtained on a realistic simulated scenario are very encouraging.
Starting Page	1
Ending Page	5
File Size	66758
Page Count	5
File Format	PDF
ISBN	9781467362986
DOI	10.1109/ECMSM.2013.6648952
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-06-24
Publisher Place	France
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Bayesian methods IEEE Xplore Estimation Portable document format Particle filters Hyperspectral image Semi-blind source separation
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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Separation of stellar spectra from hyperspectral images using particle filtering constrained by a parametric spatial mixing model

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