NDLI: A joint band prioritization and band-decorrelation approach to band selection for hyperspectral image classification

Content Provider	IEEE Xplore Digital Library
Author	Chein-I Chang Qian Du Tzu-Lung Sun Althouse, M.L.G.
Copyright Year	1980
Abstract	Band selection for remotely sensed image data is an effective means to mitigate the curse of dimensionality. Many criteria have been suggested in the past for optimal band selection. In this paper, a joint band-prioritization and band-decorrelation approach to band selection is considered for hyperspectral image classification. The proposed band prioritization is a method based on the eigen (spectral) decomposition of a matrix from which a loading-factors matrix can be constructed for band prioritization via the corresponding eigenvalues and eigenvectors. Two approaches are presented, principal components analysis (PCA)-based criteria and classification-based criteria. The former includes the maximum-variance PCA and maximum SNR PCA, whereas the latter derives the minimum misclassification canonical analysis (MMCA) (i.e., Fisher's discriminant analysis) and subspace projection-based criteria. Since the band prioritization does not take spectral correlation into account, an information-theoretic criterion called divergence is used for band decorrelation. Finally, the band selection can then be done by an eigenanalysis based band prioritization in conjunction with a divergence-based band decorrelation. It is shown that the proposed band-selection method effectively eliminates a great number of insignificant bands. Surprisingly, the experiments show that with a proper band selection, less than 0.1 of the total number of bands can achieve comparable performance using the number of full bands. This further demonstrates that the band selection can significantly reduce data volume so as to achieve data compression.
Sponsorship	IEEE Geoscience and Remote Sensing Society IEEE URSI
Starting Page	2631
Ending Page	2641
Page Count	11
File Size	630068
File Format	PDF
ISSN	01962892
Volume Number	37
Issue Number	6
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1999-11-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Hyperspectral imaging Hyperspectral sensors Principal component analysis Decorrelation Matrix decomposition Data compression Image classification Multispectral imaging Sun Eigenvalues and eigenfunctions
Content Type	Text
Resource Type	Article
Subject	Earth and Planetary Sciences Electrical and Electronic Engineering

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