Tensor discriminative locality alignment for hyperspectral image spectral-spatial feature extraction

Zhang, L; Tao, D; Huang, X

Tensor discriminative locality alignment for hyperspectral image spectral-spatial feature extraction

Zhang, L Tao, D

Huang, X

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Publication Type:: Journal Article
Citation:: IEEE Transactions on Geoscience and Remote Sensing, 2013, 51 (1), pp. 242 - 256
Issue Date:: 2013-01-01

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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.date.issued	2013-01-01	en_US
dc.identifier.citation	IEEE Transactions on Geoscience and Remote Sensing, 2013, 51 (1), pp. 242 - 256	en_US
dc.identifier.issn	0196-2892	en_US
dc.identifier.uri	http://hdl.handle.net/10453/27543
dc.description.abstract	In this paper, we propose a method for the dimensionality reduction (DR) of spectral-spatial features in hyperspectral images (HSIs), under the umbrella of multilinear algebra, i.e., the algebra of tensors. The proposed approach is a tensor extension of conventional supervised manifold-learning-based DR. In particular, we define a tensor organization scheme for representing a pixel's spectral-spatial feature and develop tensor discriminative locality alignment (TDLA) for removing redundant information for subsequent classification. The optimal solution of TDLA is obtained by alternately optimizing each mode of the input tensors. The methods are tested on three public real HSI data sets collected by hyperspectral digital imagery collection experiment, reflective optics system imaging spectrometer, and airborne visible/infrared imaging spectrometer. The classification results show significant improvements in classification accuracies while using a small number of features. © 2012 IEEE.	en_US
dc.relation	http://purl.org/au-research/grants/arc/DP120103730
dc.relation.ispartof	IEEE Transactions on Geoscience and Remote Sensing	en_US
dc.relation.isbasedon	10.1109/TGRS.2012.2197860	en_US
dc.subject.classification	Geological & Geomatics Engineering	en_US
dc.title	Tensor discriminative locality alignment for hyperspectral image spectral-spatial feature extraction	en_US
dc.type	Journal Article
utslib.citation.volume	1	en_US
utslib.citation.volume	51	en_US
utslib.for	0906 Electrical and Electronic Engineering	en_US
utslib.for	0404 Geophysics	en_US
utslib.for	0909 Geomatic Engineering	en_US
dc.location.activity	Swinburne Univ Technol, Melbourne, AUSTRALIA
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.issue	1	en_US
pubs.publication-status	Published	en_US
pubs.volume	51	en_US

Abstract:

In this paper, we propose a method for the dimensionality reduction (DR) of spectral-spatial features in hyperspectral images (HSIs), under the umbrella of multilinear algebra, i.e., the algebra of tensors. The proposed approach is a tensor extension of conventional supervised manifold-learning-based DR. In particular, we define a tensor organization scheme for representing a pixel's spectral-spatial feature and develop tensor discriminative locality alignment (TDLA) for removing redundant information for subsequent classification. The optimal solution of TDLA is obtained by alternately optimizing each mode of the input tensors. The methods are tested on three public real HSI data sets collected by hyperspectral digital imagery collection experiment, reflective optics system imaging spectrometer, and airborne visible/infrared imaging spectrometer. The classification results show significant improvements in classification accuracies while using a small number of features. © 2012 IEEE.

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