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Titolo: Interference and noiseadjusted principal components analysis
Autore: Chang, CI; Du, Q;
 Indirizzi:
 Univ Maryland, Dept Comp Sci & Elect Engn, Remote Sensing Signal & Image Proc Lab, Baltimore, MD 21250 USA Univ Maryland Baltimore MD USA 21250 ge Proc Lab, Baltimore, MD 21250 USA
 Titolo Testata:
 IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
fascicolo: 5,
volume: 37,
anno: 1999,
parte:, 2
pagine: 2387  2396
 SICI:
 01962892(199909)37:5<2387:IANPCA>2.0.ZU;2Y
 Fonte:
 ISI
 Lingua:
 ENG
 Soggetto:
 HYPERSPECTRAL IMAGERY; TRANSFORM; CLASSIFICATION;
 Keywords:
 interference annihilation; interferenceannihilated noisewhitened principal components analysis (IANWPCA); interference and noiseadjusted principal components analysis (INAPCA); maximum noise fraction (MNF) transformation; noiseadjusted principal components (NAPC); transform; principal components analysis (PCA); signal to interference plus noisebased principal components analysis (SINRPCA);
 Tipo documento:
 Article
 Natura:
 Periodico
 Settore Disciplinare:
 Engineering, Computing & Technology
 Citazioni:
 15
 Recensione:
 Indirizzi per estratti:
 Indirizzo: Chang, CI Univ Maryland, Dept Comp Sci & Elect Engn, Remote Sensing Signal& Image Proc Lab, Baltimore, MD 21250 USA Univ Maryland Baltimore MD USA 21250 b, Baltimore, MD 21250 USA



 Citazione:
 C.I. Chang e Q. Du, "Interference and noiseadjusted principal components analysis", IEEE GEOSCI, 37(5), 1999, pp. 23872396
Abstract
The goal of principal components analysis (PCA) is to find principal components in accordance with maximum variance of a data matrix, However, it hasbeen shown recently that such variancebased principal components may not adequately represent image quality. As a result, a modified PCA approach based on maximization of SNR was proposed, Called maximum noise fraction (MNF) transformation or noiseadjusted principal components (NAPC) transform, it arranges principal components in decreasing order of image quality ratherthan variance. One of the major disadvantages of this approach is that thenoise covariance matrix must be estimated accurately from the data a priori, Another is that the factor of interference is not taken into account in MNF or NAPC in which the interfering effect tends to be more serious than noise in hyperspectral images, In this paper, these two problems are addressed by considering the interference as a separate, unknown signal source, from which an interference and noiseadjusted principal components analysis (INAPCA) can be developed in a manner similar to the one from which the NAPCwas derived. Two approaches are proposed for the INAPCA, referred to as signal to interference plus noise ratiobased principal components analysis (SINRPCA) and interferenceannihilated noisewhitened principal components analysis (IANWPCA), It is shown that if interference is taken care of properly, SINRPCA and IANWPCA significantly improve NAPC. In addition, interference annihilation also improves the estimation of the noise covariance matrix. All of these results are compared with NAPC and PCA and are demonstrated by HYDICE data.
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Documento generato il 02/12/20 alle ore 18:09:23