NDLI: Nonlinear Deconvolution of Hyperspectral Data With MCMC for Studying the Kinematics of Galaxies

Content Provider	IEEE Xplore Digital Library
Author	Villeneuve, E. Carfantan, H.
Copyright Year	1992
Abstract	Hyperspectral imaging has been an area of active research in image processing and analysis for more than 10 years, mainly for remote sensing applications. Astronomical ground-based hyperspectral imagers offer new challenges to the community, which differ from the previous ones in the nature of the observed objects, but also in the quality of the data, with a low signal-to-noise ratio and a low resolution, due to the atmospheric turbulence. In this paper, we focus on a deconvolution problem specific to hyperspectral astronomical data, to improve the study of the kinematics of galaxies. The aim is to estimate the flux, the relative velocity, and the velocity dispersion, integrated along the line-of-sight, for each spatial pixel of an observed galaxy. Thanks to the Doppler effect, this is equivalent to estimate the amplitude, center, and width of spectral emission lines, in a small spectral range, for every spatial pixel of the hyperspectral data. We consider a parametric model for the spectral lines and propose to compute the posterior mean estimators, in a Bayesian framework, using Monte Carlo Markov chain algorithms. Various estimation schemes are proposed for this nonlinear deconvolution problem, taking advantage of the linearity of the model with respect to the flux parameters. We differentiate between methods taking into account the spatial blurring of the data (deconvolution) or not (estimation). The performances of the methods are compared with classical ones, on two simulated data sets. It is shown that the proposed deconvolution method significantly improves the resolution of the estimated kinematic parameters.
Sponsorship	IEEE Signal Processing Society
Starting Page	4322
Ending Page	4335
Page Count	14
File Size	2148360
File Format	PDF
ISSN	10577149
Volume Number	23
Issue Number	10
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-01-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Hyperspectral imaging Estimation Deconvolution Data models Noise Kinematics inverse problems parameter estimation Bayes methods Monte Carlo methods astrophysics deconvolution
Content Type	Text
Resource Type	Article
Subject	Computer Graphics and Computer-Aided Design Software

Sl.	Authority	Responsibilities	Communication Details
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
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Unsupervised Unmixing of Hyperspectral Images Accounting for Endmember Variability

Unsupervised Post-Nonlinear Unmixing of Hyperspectral Images Using a Hamiltonian Monte Carlo Algorithm

Cramer-Rao bounds for parametric shape estimation in inverse problems

Regularization parameter estimation for non-negative hyperspectral image deconvolution

An adaptive Gaussian model for satellite image deblurring

Collaborative sparse regression using spatially correlated supports - Application to hyperspectral unmixing

Bayesian function learning using MCMC methods

Fusion of Hidden Markov Random Field Models and Its Bayesian Estimation

Semi-Blind Sparse Image Reconstruction With Application to MRFM

Nonlinear Deconvolution of Hyperspectral Data With MCMC for Studying the Kinematics of Galaxies

Similar Documents

Unsupervised Unmixing of Hyperspectral Images Accounting for Endmember Variability

Unsupervised Post-Nonlinear Unmixing of Hyperspectral Images Using a Hamiltonian Monte Carlo Algorithm

Cramer-Rao bounds for parametric shape estimation in inverse problems

Regularization parameter estimation for non-negative hyperspectral image deconvolution

An adaptive Gaussian model for satellite image deblurring

Collaborative sparse regression using spatially correlated supports - Application to hyperspectral unmixing

Bayesian function learning using MCMC methods

Fusion of Hidden Markov Random Field Models and Its Bayesian Estimation

Semi-Blind Sparse Image Reconstruction With Application to MRFM

Nonlinear Deconvolution of Hyperspectral Data With MCMC for Studying the Kinematics of Galaxies