Clustering Regression Wavelet Analysis for Lossless Compression of Hyperspectral Imagery

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

3 Scopus citations


Recently, Regression Wavelet Analysis (RWA) was proposed as a method for lossless compression of hyperspectral images. In RWA, a linear regression is performed after a spectral wavelet transform to generate predictors which estimate the detail coefficients from approximation coefficients at each scale of the spectral wavelet transform. In this work, we propose Clustering Regression Wavelet Analysis (RWA-C), an extension of the original 'Restricted' RWA model which may be used to improve compression performance while maintaining component scalability. We demonstrate that clustering may be used to group pixels with similar spectral profiles. These clusters may then be more efficiently processed to improve RWA prediction performance while only requiring a modest increase side-information and computational complexity.

Original languageEnglish (US)
Title of host publicationProceedings - DCC 2019
Subtitle of host publication2019 Data Compression Conference
EditorsAli Bilgin, Michael W. Marcellin, Joan Serra-Sagrista, James A. Storer
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages1
ISBN (Electronic)9781728106571
StatePublished - May 10 2019
Event2019 Data Compression Conference, DCC 2019 - Snowbird, United States
Duration: Mar 26 2019Mar 29 2019

Publication series

NameData Compression Conference Proceedings
ISSN (Print)1068-0314


Conference2019 Data Compression Conference, DCC 2019
Country/TerritoryUnited States


  • Compression
  • Hyperspectral image
  • Remote sensing
  • Wavelets

ASJC Scopus subject areas

  • Computer Networks and Communications


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