Hyperspectral image classification using spectral and spatial information based linear discriminant analysis

Li, CH; Chu, HS; Kuo, BC; Lin, CT

Hyperspectral image classification using spectral and spatial information based linear discriminant analysis

Li, CH Chu, HS Kuo, BC Lin, CT

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Publication Type:: Conference Proceeding
Citation:: International Geoscience and Remote Sensing Symposium (IGARSS), 2011, pp. 1716 - 1719
Issue Date:: 2011-11-16

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Field	Value	Language
dc.contributor.author	Li, CH	en_US
dc.contributor.author	Chu, HS	en_US
dc.contributor.author	Kuo, BC	en_US
dc.contributor.author	Lin, CT https://orcid.org/0000-0001-8371-8197	en_US
dc.date.issued	2011-11-16	en_US
dc.identifier.citation	International Geoscience and Remote Sensing Symposium (IGARSS), 2011, pp. 1716 - 1719	en_US
dc.identifier.isbn	9781457710056	en_US
dc.identifier.uri	http://hdl.handle.net/10453/119499
dc.description.abstract	Feature extraction plays an essential role in Hyperspectral image classification. Linear discriminant analysis (LDA) is a commonly used feature extraction (FE) method to resolve the Hughes phenomenon for classification. The Hughes phenomenon (also called the curse of dimensionality) is often encountered in classification when the dimensionality of the space grows and the size of the training set is fixed, especially in the small sampling size problem. Recent studies show that the spatial information can greatly improve the classification performance. Hence, for hyperspectral image classification, it is not only necessary to use the available spectral information but also to exploit the spatial information. In this paper, spatial information is acquired by the concept of the Markov random field (MRF), and this spatial information is used to form the membership values of every pixel in the hyperspectral image. The experimental results on two hyperspectral images, the Washington DC Mall and the Indian Pine Site, show that the proposed method can yield a better classification performance than LDA in the small sampling size problem. © 2011 IEEE.	en_US
dc.relation.ispartof	International Geoscience and Remote Sensing Symposium (IGARSS)	en_US
dc.relation.isbasedon	10.1109/IGARSS.2011.6049566	en_US
dc.title	Hyperspectral image classification using spectral and spatial information based linear discriminant analysis	en_US
dc.type	Conference Proceeding
utslib.for	0906 Electrical and Electronic 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
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

Feature extraction plays an essential role in Hyperspectral image classification. Linear discriminant analysis (LDA) is a commonly used feature extraction (FE) method to resolve the Hughes phenomenon for classification. The Hughes phenomenon (also called the curse of dimensionality) is often encountered in classification when the dimensionality of the space grows and the size of the training set is fixed, especially in the small sampling size problem. Recent studies show that the spatial information can greatly improve the classification performance. Hence, for hyperspectral image classification, it is not only necessary to use the available spectral information but also to exploit the spatial information. In this paper, spatial information is acquired by the concept of the Markov random field (MRF), and this spatial information is used to form the membership values of every pixel in the hyperspectral image. The experimental results on two hyperspectral images, the Washington DC Mall and the Indian Pine Site, show that the proposed method can yield a better classification performance than LDA in the small sampling size problem. © 2011 IEEE.

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http://hdl.handle.net/10453/119499