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Titolo:
Interference and noise-adjusted 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:
0196-2892(199909)37:5<2387:IANPCA>2.0.ZU;2-Y
Fonte:
ISI
Lingua:
ENG
Soggetto:
HYPERSPECTRAL IMAGERY; TRANSFORM; CLASSIFICATION;
Keywords:
interference annihilation; interference-annihilated noise-whitened principal components analysis (IANW-PCA); interference and noise-adjusted principal components analysis (INAPCA); maximum noise fraction (MNF) transformation; noise-adjusted principal components (NAPC); transform; principal components analysis (PCA); signal to interference plus noise-based principal components analysis (SINR-PCA);
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 noise-adjusted principal components analysis", IEEE GEOSCI, 37(5), 1999, pp. 2387-2396

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 variance-based 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 noise-adjusted 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 noise-adjusted 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 ratio-based principal components analysis (SINR-PCA) and interference-annihilated noise-whitened principal components analysis (IANW-PCA), It is shown that if interference is taken care of properly, SINR-PCA and IANW-PCA 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