NDLI: Locality-preserving nonnegative matrix factorization for hyperspectral image classification

Content Provider	IEEE Xplore Digital Library
Author	Wei Li Prasad, S. Fowler, J.E. Cui, M.
Copyright Year	2012
Description	Author affiliation: Department of Electrical and Computer Engineering, Mississippi State University, USA (Wei Li; Fowler, J.E.) \|\| Department of Electrical and Computer Engineering, University of Houston, USA (Prasad, S.; Cui, M.)
Abstract	Feature extraction based on nonnegative matrix factorization is considered for hyperspectral image classification. One shortcoming of most remote-sensing data is low spatial resolution, which causes a pixel to be mixed with several pure spectral signatures, or endmembers. To counter this effect, locality-preserving nonnegative matrix factorization is employed in order to extract an endmembers-based feature representation as well as to preserve the intrinsic geometric structure of hyperspectral data. Subsequently, a Gaussian mixture model classifier is employed in the induced-feature subspace. Experimental results demonstrate that the proposed classification system significantly outperforms traditional approaches even in instances of limited training data and severe pixel mixing.
Starting Page	1405
Ending Page	1408
File Size	125207
Page Count	4
File Format	PDF
ISBN	9781467311601
ISSN	21536996
e-ISBN	9781467311595
e-ISBN	9781467311588
DOI	10.1109/IGARSS.2012.6351273
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 Feature extraction Training Accuracy Educational institutions Support vector machines pattern classification Linear mixing model nonnegative matrix factorization feature extraction
Content Type	Text
Resource Type	Article

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