Enhancing Hyperspectral Data Throughput Utilizing Wavelet-Based Fingerprints

Conference · Wed Sep 01 04:00:00 EDT 1999

OSTI ID:764641

Ginsberg, I W ^[1]

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1. RSL Bechtel Nevada

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.

• Research Organization: Bechtel Nevada Corp. (US)
• Sponsoring Organization: US Department of Energy (US)
• DOE Contract Number: AC08-96NV11718
• OSTI ID: 764641
• Report Number(s): DOE/NV/11718--366
• Country of Publication: United States
• Language: English

Related Subjects

99 GENERAL AND MISCELLANEOUS
ALGORITHM
ALGORITHMS
COMPUTATIONAL EXPENSE
COMPUTER CALCULATIONS
DATA ANALYSIS
FEATURE EXTRACTION
HYPERSPECTRAL
MULTIRESOLUTION
PATTERN RECOGNITION
SPECTRA
WAVELET