DocumentCode :
3049739
Title :
Discrimination and identification for subpixel targets in hyperspectral imagery
Author :
Chang, Chein-I ; Liu, Weimin ; Chang, Chein-Chi
Author_Institution :
Dept. of Comput. Sci. & Electr. Eng., Maryland Univ., Baltimore, MD, USA
Volume :
5
fYear :
2004
fDate :
24-27 Oct. 2004
Firstpage :
3339
Abstract :
Spectral measures have been used in material identification and discrimination. They are effective if the spectral signatures are calibrated and not contaminated. However, it may not be true in many real applications, specifically, for mixed pixels and subpixel targets. This paper investigates the issue of discrimination and identification for subpixel targets and further develops sample spectral covariance/correlation matrix-based hyperspectral measures to account for spectral variability within subpixel targets. Two types of measures are of interest and studied, Mahalanobis distance-based hyperspectral measures and matched filter-based hyperspectral measures. In order to substantiate the proposed measures, a real data-based comparative analysis is conducted and compared to two spectral similarity measures, spectral angle mapper (SAM) and spectral information divergence (SID) for performance evaluation. The experiments show that both Mahalanobis distance-based hyperspectral measures and matched filter-based hyperspectral measures work very effectively and outperformed the SAM and the SID in discrimination and identification for subpixel targets.
Keywords :
covariance matrices; geophysical signal processing; image resolution; matched filters; Mahalanobis distance-based hyperspectral measures; correlation matrix-based hyperspectral measure; hyperspectral imagery; matched filter-based hyperspectral measures; material identification; mixed pixels; real data-based comparative analysis; spectral angle mapper; spectral covariance; spectral information divergence; spectral signatures; spectral variability; subpixel target discrimination; subpixel target identification; Covariance matrix; Hyperspectral imaging; Hyperspectral sensors; Image processing; Matched filters; Performance analysis; Performance evaluation; Pollution measurement; Remote sensing; Signal processing;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Image Processing, 2004. ICIP '04. 2004 International Conference on
ISSN :
1522-4880
Print_ISBN :
0-7803-8554-3
Type :
conf
DOI :
10.1109/ICIP.2004.1421829
Filename :
1421829
Link To Document :
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