NDLI: Sparsity and morphological diversity for hyperspectral data analysis

Content Provider	IEEE Xplore Digital Library
Author	Bobin, J. Moudden, Y. Starck, J.-L. Fadili, J.
Copyright Year	2009
Description	Author affiliation: CEA, Saclay, IRFU, SEDI/Service d'Astrophysique, Orme des Merisiers 91191 Gif-sur-Yvette, France (Moudden, Y.; Starck, J.-L.) \|\| GREYC CNRS UMR 6072, Image Processing Group, ENSICAEN 14050, Caen Cedex, France (Fadili, J.) \|\| California Institute of Technology, Applied and Computational Mathematics, 217-50 Pasadena, 91125, USA (Bobin, J.)
Abstract	Recently morphological diversity and sparsity have emerged as new and effective sources of diversity for Blind Source Separation. Based on these new concepts, novelmethods such as Generalized Morphological Component Analysis have been put forward. The latter takes advantage of the very sparse representation of structured data in large overcomplete dictionaries, to separate sources based on their morphology. Building on GMCA, the purpose of this contribution is to describe a new algorithm for hyperspectral data processing. Large-scale hyperspectral data refers to collected data that exhibit sparse spectral signatures in addition to sparse spatial morphologies, in specified dictionaries of spectral and spatial waveforms. Numerical experiments are reported which demonstrate the validity of the proposed extension for solving source separation problems involving hyperspectral data.
Starting Page	1481
Ending Page	1484
File Size	300676
Page Count	4
File Format	PDF
ISBN	9781424456536
ISSN	15224880
e-ISBN	9781424456543
e-ISBN	9781424456550
DOI	10.1109/ICIP.2009.5414547
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-11-07
Publisher Place	Egypt
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Hyperspectral imaging Data analysis Hyperspectral sensors Source separation Data processing Dictionaries Iterative algorithms Blind source separation Morphology Mathematics astronomy source separation hyperspectral data sparsity remote sensing wavelets
Content Type	Text
Resource Type	Article

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

Hyperspectral BSS Using GMCA With Spatio-Spectral Sparsity Constraints

Sparsity and Morphological Diversity in Blind Source Separation

Morphological diversity and sparsity : new insights into multivariate data analysis

An efficient wavelet dictionary for texture separation

On the use of ICA for hyperspectral image analysis

Sparse blind source separation for partially correlated sources

Blind source separation: the sparsity revolution (2008).

Morphological diversity and source separation

Enhanced Source Separation by Morphological Component Analysis

Sparsity and morphological diversity for hyperspectral data analysis

Similar Documents

Hyperspectral BSS Using GMCA With Spatio-Spectral Sparsity Constraints

Sparsity and Morphological Diversity in Blind Source Separation

Morphological diversity and sparsity : new insights into multivariate data analysis

An efficient wavelet dictionary for texture separation

On the use of ICA for hyperspectral image analysis

Sparse blind source separation for partially correlated sources

Blind source separation: the sparsity revolution (2008).

Morphological diversity and source separation

Enhanced Source Separation by Morphological Component Analysis

Sparsity and morphological diversity for hyperspectral data analysis