A dynamic subspace method for hyperspectral image classification

Yang, JM; Kuo, BC; Yu, PT; Chuang, CH

A dynamic subspace method for hyperspectral image classification

Yang, JM Kuo, BC Yu, PT Chuang, CH

Permalink

Publication Type:: Journal Article
Citation:: IEEE Transactions on Geoscience and Remote Sensing, 2010, 48 (7), pp. 2840 - 2853
Issue Date:: 2010-07-01

Closed Access

	Filename	Description	Size
	A Dynamic Subspace Method for Hyperspectral Image Classification.pdf	Published Version	2.71 MB	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	Yang, JM	en_US
dc.contributor.author	Kuo, BC	en_US
dc.contributor.author	Yu, PT	en_US
dc.contributor.author	Chuang, CH https://orcid.org/0000-0002-5043-8380	en_US
dc.date.issued	2010-07-01	en_US
dc.identifier.citation	IEEE Transactions on Geoscience and Remote Sensing, 2010, 48 (7), pp. 2840 - 2853	en_US
dc.identifier.issn	0196-2892	en_US
dc.identifier.uri	http://hdl.handle.net/10453/43803
dc.description.abstract	Many studies have demonstrated that multiple classifier systems, such as the random subspace method (RSM), obtain more outstanding and robust results than a single classifier on extensive pattern recognition issues. In this paper, we propose a novel subspace selection mechanism, named the dynamic subspace method (DSM), to improve RSM on automatically determining dimensionality and selecting component dimensions for diverse subspaces. Two importance distributions are proposed to impose on the process of constructing ensemble classifiers. One is the distribution of subspace dimensionality, and the other is the distribution of band weights. Based on the two distributions, DSM becomes an automatic, dynamic, and adaptive ensemble. The real data experimental results show that the proposed DSM obtains sound performances than RSM, and that the classification maps remarkably produce fewer speckles. © 2006 IEEE.	en_US
dc.relation.ispartof	IEEE Transactions on Geoscience and Remote Sensing	en_US
dc.relation.isbasedon	10.1109/TGRS.2010.2043533	en_US
dc.subject.classification	Geological & Geomatics Engineering	en_US
dc.title	A dynamic subspace method for hyperspectral image classification	en_US
dc.type	Journal Article
utslib.citation.volume	7	en_US
utslib.citation.volume	48	en_US
utslib.for	0404 Geophysics	en_US
utslib.for	0906 Electrical and Electronic Engineering	en_US
utslib.for	0909 Geomatic Engineering	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
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	closed_access
pubs.issue	7	en_US
pubs.publication-status	Published	en_US
pubs.volume	48	en_US

Abstract:

Many studies have demonstrated that multiple classifier systems, such as the random subspace method (RSM), obtain more outstanding and robust results than a single classifier on extensive pattern recognition issues. In this paper, we propose a novel subspace selection mechanism, named the dynamic subspace method (DSM), to improve RSM on automatically determining dimensionality and selecting component dimensions for diverse subspaces. Two importance distributions are proposed to impose on the process of constructing ensemble classifiers. One is the distribution of subspace dimensionality, and the other is the distribution of band weights. Based on the two distributions, DSM becomes an automatic, dynamic, and adaptive ensemble. The real data experimental results show that the proposed DSM obtains sound performances than RSM, and that the classification maps remarkably produce fewer speckles. © 2006 IEEE.

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

http://hdl.handle.net/10453/43803