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Proceedings Paper

Wavelet analysis of hyperspectral reflectance data for spectral feature extraction
Author(s): Guiling Sun; Yonghua Fang; Cuilan Zhang; Xianbing Wang; Benyong Yang
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Paper Abstract

This study determined the potential of wavelet-based analysis for extracting spectral features of hyperspectral reflectance signals. The dyadic discrete wavelet transform is proposed for feature extraction from a high dimensional data space. The wavelet's inherent multi-resolution properties are discussed in terms related to multi-spectral and hyperspectral remote sensing. Wavelet can focus on the local structure of the signal through adjusting the scale parameter in the course of focusing. So we can find the singularities and the inflexions of the original signal. The absorption strips are thus detected consequently with the local wavelet transform modulus (absolute value) maxima. The results show a superior performance of the proposed wavelet-based features that are more meaningful for spectral feature extraction when compared to conventional methods.

Paper Details

Date Published: 12 May 2005
PDF: 9 pages
Proc. SPIE 5832, Optical Technologies for Atmospheric, Ocean, and Environmental Studies, (12 May 2005); doi: 10.1117/12.619869
Show Author Affiliations
Guiling Sun, Anhui Institute of Optics and Fine Mechanics, CAS (China)
Yonghua Fang, Anhui Institute of Optics and Fine Mechanics, CAS (China)
Cuilan Zhang, Anhui Institute of Optics and Fine Mechanics, CAS (China)
Xianbing Wang, Anhui Institute of Optics and Fine Mechanics, CAS (China)
Benyong Yang, Anhui Institute of Optics and Fine Mechanics, CAS (China)


Published in SPIE Proceedings Vol. 5832:
Optical Technologies for Atmospheric, Ocean, and Environmental Studies
Daren Lu; Gennadii G. Matvienko, Editor(s)

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