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Titolo:
A joint band prioritization and band-decorrelation approach to band selection for hyperspectral image classification
Autore:
Chang, CI; Du, Q; Sun, TL; Althouse, MLG;
Indirizzi:
Univ Maryland Baltimore Cty, Dept Elect Engn & Comp Sci, Remote Sensing Signal & Image Proc Lab, Baltimore, MD 21250 USA Univ Maryland Baltimore CtyBaltimore MD USA 21250 altimore, MD 21250 USA
Titolo Testata:
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
fascicolo: 6, volume: 37, anno: 1999,
pagine: 2631 - 2641
SICI:
0196-2892(199911)37:6<2631:AJBPAB>2.0.ZU;2-9
Fonte:
ISI
Lingua:
ENG
Soggetto:
PRINCIPAL COMPONENTS TRANSFORM; SUBSPACE PROJECTION APPROACH; REMOTE-SENSING DATA; NOISE;
Keywords:
band decorrelation; band prioritization; band selection; divergence; eigenanalysis; hyperspectral classification; orthogonal-subspace projection (OSP); principal-components analysis (PCA);
Tipo documento:
Article
Natura:
Periodico
Settore Disciplinare:
Engineering, Computing & Technology
Citazioni:
23
Recensione:
Indirizzi per estratti:
Indirizzo: Chang, CI Univ Maryland Baltimore Cty, Dept Elect Engn & Comp Sci, Remote Sensing Signal & Image Proc Lab, Baltimore, MD 21250 USA Univ Maryland Baltimore Cty Baltimore MD USA 21250 MD 21250 USA
Citazione:
C.I. Chang et al., "A joint band prioritization and band-decorrelation approach to band selection for hyperspectral image classification", IEEE GEOSCI, 37(6), 1999, pp. 2631-2641

Abstract

Band selection for remotely sensed image data is an effective means to mitigate the curse of dimensionality, Many criteria have been suggested in thepast 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 methodbased 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 forband decorrelation, Finally, the band selection can then be done by an eigenanalysis-based band prioritization in conjunction with a divergence-basedband 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 thetotal 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.

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Documento generato il 02/12/20 alle ore 18:19:30