NDLI: Local linear spectral unmixing via cluster analysis and non-negative matrix factorization for hyperspectral (CHRIS/PROBA) imagery

Content Provider	IEEE Xplore Digital Library
Author	Lazar, C. Demarchi, L. Steenhoff, D. Chan, J.C.-W. Nowe, A. Sahli, H.
Copyright Year	2012
Description	Author affiliation: Cartography and GIS Research Group (CGIS), Dept. of Geography, Vrije Universiteit Brussel Pleinlaan 2 B-1050 Brussels Belgium (Demarchi, L.) \|\| Computational Modeling Lab (COMO), Dept. of Computer Science, Vrije Universiteit Brussel Pleinlaan 2 B-1050 Brussels Belgium (Lazar, C.; Steenhoff, D.; Nowe, A.) \|\| Department of Electronics and Informatics (ETRO), Vrije Universiteit Brussel Pleinlaan 2 B-1050 Brussels Belgium (Chan, J.C.-W.; Sahli, H.)
Abstract	We present a novel approach for spectral unmixing in hyperspectral imagery called local linear spectral unmixing (LLSU). Our new proposal relies on an existing strategy for non-linear data modelling where a general non-linear model is approximated via several piecewise linear models. The algorithm is the result of hybridizing two well-known strategies for exploratory high-dimensional data analysis: cluster analysis and non-negative matrix factorization. It has been proposed to answer the limitations of global linear unmixing models which are widely used for spectral unmixing. Our strategy is to first group similar pixels from the hyperspctral image via cluster analysis and then to consider a local linear unmixing model for each cluster in order to obtain the constituent endmembers. Subsequently, the resulted local endmembers from each cluster of mixed pixels are at their turn clustered based on their spectral similarity; the final solution is given by the clusters' centroids.
Starting Page	7267
Ending Page	7270
File Size	515571
Page Count	4
File Format	PDF
ISBN	9781467311601
ISSN	21536996
e-ISBN	9781467311595
e-ISBN	9781467311588
DOI	10.1109/IGARSS.2012.6351984
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-07-22
Publisher Place	Germany
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Hyperspectral imaging Analytical models Data models Probabilistic logic Signal processing algorithms piece-wise linear models Spectral unmixing hyperspectral imagery cluster analysis K-means non-negative matrix factorization local linear models non-linear models
Content Type	Text
Resource Type	Article

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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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