Nonnegative discriminative manifold learning for hyperspectral data dimension reduction

Zhang, L; Tao, D; Huang, X; Xia, GS

Nonnegative discriminative manifold learning for hyperspectral data dimension reduction

Zhang, L Tao, D

Huang, X Xia, GS

Permalink

Publication Type:: Conference Proceeding
Citation:: Workshop on Hyperspectral Image and Signal Processing, Evolution in Remote Sensing, 2013, 2013-June
Issue Date:: 2013-06-28

Closed Access

	Filename	Description	Size
	08080702.pdf	Published version	336.77 kB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Closed Access

This item is closed access and not available.

Full metadata record

Field	Value	Language
dc.contributor.author	Zhang, L	en_US
dc.contributor.author	Tao, D https://orcid.org/0000-0001-7225-5449	en_US
dc.contributor.author	Huang, X	en_US
dc.contributor.author	Xia, GS	en_US
dc.date.issued	2013-06-28	en_US
dc.identifier.citation	Workshop on Hyperspectral Image and Signal Processing, Evolution in Remote Sensing, 2013, 2013-June	en_US
dc.identifier.isbn	9781509011193	en_US
dc.identifier.issn	2158-6276	en_US
dc.identifier.uri	http://hdl.handle.net/10453/127225
dc.description.abstract	© 2013 IEEE. Manifold learning algorithms have been demonstrated to be effective for hyperspectral data dimension reduction (DR). However, the low dimensional feature representation resulted by traditional manifold learning algorithms can not preserve the nonnegativity property of the hyperspectral data, which leads inconsistency with the psychological intuition of 'combining parts to form a whole'. In this paper, we propose a nonnegative discriminative manifold learning (NDML) algorithm for hyperspectral data DR, which yields a discriminative and low dimensional feature representation, with psychological and physical evidence in the human brain. Our method benefits from both the nonnegative matrix factorization (NMF) algorithm and the discriminative manifold learning (DML) algorithm. We apply the NDML algorithm to hyperspectral remote sensing image classification on HYDICE dataset. Experimental results confirm the efficiency of the proposed NDML algorithm, compared with some existing manifold learning based DR methods.	en_US
dc.relation.ispartof	Workshop on Hyperspectral Image and Signal Processing, Evolution in Remote Sensing	en_US
dc.relation.isbasedon	10.1109/WHISPERS.2013.8080702	en_US
dc.title	Nonnegative discriminative manifold learning for hyperspectral data dimension reduction	en_US
dc.type	Conference Proceeding
utslib.citation.volume	2013-June	en_US
utslib.for	0802 Computation Theory and Mathematics	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US
pubs.volume	2013-June	en_US

Abstract:

© 2013 IEEE. Manifold learning algorithms have been demonstrated to be effective for hyperspectral data dimension reduction (DR). However, the low dimensional feature representation resulted by traditional manifold learning algorithms can not preserve the nonnegativity property of the hyperspectral data, which leads inconsistency with the psychological intuition of 'combining parts to form a whole'. In this paper, we propose a nonnegative discriminative manifold learning (NDML) algorithm for hyperspectral data DR, which yields a discriminative and low dimensional feature representation, with psychological and physical evidence in the human brain. Our method benefits from both the nonnegative matrix factorization (NMF) algorithm and the discriminative manifold learning (DML) algorithm. We apply the NDML algorithm to hyperspectral remote sensing image classification on HYDICE dataset. Experimental results confirm the efficiency of the proposed NDML algorithm, compared with some existing manifold learning based DR methods.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/127225