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Title: Enhancing Hyperspectral Data Throughput Utilizing Wavelet-Based Fingerprints

Abstract

Multiresolutional decompositions known as spectral fingerprints are often used to extract spectral features from multispectral/hyperspectral data. In this study, the authors 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: (a) the computational expense of the new method is compared with the computational costs of the current method and (b) 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.

Authors:
Publication Date:
Research Org.:
Bechtel Nevada Corp. (US)
Sponsoring Org.:
US Department of Energy (US)
OSTI Identifier:
764641
Report Number(s):
DOE/NV/11718-366
TRN: AH200034%%27
DOE Contract Number:  
AC08-96NV11718
Resource Type:
Conference
Resource Relation:
Conference: EUROPTO-European Optical Society and the International Society for Optical Engineering, Florence (IT), 08/24/1999; Other Information: PBD: 1 Sep 1999
Country of Publication:
United States
Language:
English
Subject:
99 GENERAL AND MISCELLANEOUS//MATHEMATICS, COMPUTING, AND INFORMATION SCIENCE; ALGORITHMS; DATA ANALYSIS; SPECTRA; PATTERN RECOGNITION; COMPUTER CALCULATIONS; WAVELET; ALGORITHM; FEATURE EXTRACTION; MULTIRESOLUTION; HYPERSPECTRAL; COMPUTATIONAL EXPENSE

Citation Formats

I. W. Ginsberg. Enhancing Hyperspectral Data Throughput Utilizing Wavelet-Based Fingerprints. United States: N. p., 1999. Web.
I. W. Ginsberg. Enhancing Hyperspectral Data Throughput Utilizing Wavelet-Based Fingerprints. United States.
I. W. Ginsberg. Wed . "Enhancing Hyperspectral Data Throughput Utilizing Wavelet-Based Fingerprints". United States. https://www.osti.gov/servlets/purl/764641.
@article{osti_764641,
title = {Enhancing Hyperspectral Data Throughput Utilizing Wavelet-Based Fingerprints},
author = {I. W. Ginsberg},
abstractNote = {Multiresolutional decompositions known as spectral fingerprints are often used to extract spectral features from multispectral/hyperspectral data. In this study, the authors 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: (a) the computational expense of the new method is compared with the computational costs of the current method and (b) 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.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {1999},
month = {9}
}

Conference:
Other availability
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