Hyperspectral feature classification with alternate wavelet transform representations

James F. Scholl, E. Keith Hege, Michael Lloyd-Hart, Eustace L. Dereniak

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations

Abstract

The effectiveness of many hyperspectral feature extraction algorithms involving classification (and linear spectral unmixing) are dependent on the use of spectral signature libraries. If two or more signatures are roughly similar to each other, these methods which use algorithms such as singular value decomposition (SVD) or least squares to identify the object will not work well. This especially goes for these procedures which are combined with three-dimensional discrete wavelet transforms, which replace the signature libraries with their corresponding lowpass wavelet transform coefficients. In order to address this issue, alternate ways of transforming these signature libraries using bandpass or highpass wavelet transform coefficients from either wavelet or Walsh (Haar wavelet packet) transforms in the spectral direction will be described. These alternate representations of the data emphasize differences between the signatures which lead to improved classification performance as compared to existing procedures.

Original languageEnglish (US)
Title of host publicationMathematics of Data/Image Pattern Recognition, Compression, and Encryption with Applications IX
DOIs
Publication statusPublished - Nov 9 2006
EventMathematics of Data/Image Pattern Recognition, Compression, and Encryption with Applications IX - San Diego, CA, United States
Duration: Aug 15 2006Aug 16 2006

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume6315
ISSN (Print)0277-786X

Conference

ConferenceMathematics of Data/Image Pattern Recognition, Compression, and Encryption with Applications IX
CountryUnited States
CitySan Diego, CA
Period8/15/068/16/06

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Keywords

  • Classification
  • Hyperspectral signal processing
  • Remote sensing
  • Wavelet packets
  • Wavelet transforms

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering

Cite this

Scholl, J. F., Hege, E. K., Lloyd-Hart, M., & Dereniak, E. L. (2006). Hyperspectral feature classification with alternate wavelet transform representations. In Mathematics of Data/Image Pattern Recognition, Compression, and Encryption with Applications IX [63150G] (Proceedings of SPIE - The International Society for Optical Engineering; Vol. 6315). https://doi.org/10.1117/12.682583