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Proceedings Paper

Enhancing hyperspectral data throughput utilizing wavelet-based fingerprints
Author(s): Lori Mann Bruce; Jiang Li
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Paper Abstract

Multiresolutional decompositions known as spectral fingerprints are often used to extract spectral features from multispectral/hyperspectral data. In this study, we investigate the use of wavelet-based algorithms for generating spectral fingerprints. The wavelet-based algorithms are compared to the currently used method, traditional convolution with first-derivative Gaussian filters. The comparison analyses consists of two parts: (1) the computational expense of the new method is compared with the computational costs of the current method and (2) the outputs of the wavelet-based methods are compared with those of the current method to determine any practical differences in the resulting spectral fingerprints. The results show that the wavelet-based algorithms can greatly reduce the computational expense of generating spectral fingerprints, while practically no differences exist in the resulting fingerprints. The analysis is conducted on a database of hyperspectral signatures, namely, Hyperspectral Digital Image Collection Experiment (HYDICE) signatures. The reduction in computational expense is by a factor of about 30, and the average Euclidean distance between resulting fingerprints is on the order of 0.02.

Paper Details

Date Published: 14 December 1999
PDF: 10 pages
Proc. SPIE 3871, Image and Signal Processing for Remote Sensing V, (14 December 1999); doi: 10.1117/12.373260
Show Author Affiliations
Lori Mann Bruce, Univ. of Nevada/Las Vegas and Department of Energy Remote Sensing Lab. (United States)
Jiang Li, Univ. of Nevada/Las Vegas and Department of Energy Remote Sensing Lab. (United States)


Published in SPIE Proceedings Vol. 3871:
Image and Signal Processing for Remote Sensing V
Sebastiano Bruno Serpico, Editor(s)

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