Enhancing Hyperspectral Data Throughput Utilizing Wavelet-Based Fingerprints

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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 ... continued below

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Ginsberg, I. W. September 1, 1999.

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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.

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OSTI as DE00764641

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  • EUROPTO-European Optical Society and the International Society for Optical Engineering, Florence (IT), 08/24/1999

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  • Report No.: DOE/NV/11718--366
  • Grant Number: AC08-96NV11718
  • Office of Scientific & Technical Information Report Number: 764641
  • Archival Resource Key: ark:/67531/metadc718123

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  • September 1, 1999

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  • Sept. 29, 2015, 5:31 a.m.

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  • Feb. 11, 2016, 9:18 p.m.

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Ginsberg, I. W. Enhancing Hyperspectral Data Throughput Utilizing Wavelet-Based Fingerprints, article, September 1, 1999; Nevada. (digital.library.unt.edu/ark:/67531/metadc718123/: accessed August 16, 2017), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.