Hyperspectral image lossless compression algorithm based on adaptive band regrouping

Mingyi He, Lin Bai, Yuchao Dai, Jing Zhang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Scopus citations

Abstract

Hyperspectral image has weak spatial correlation and strong spectral correlation. As to exploit spectrum redundancy sufficiently, it must be pre-processed. In this paper, a new algorithm for lossless compression of hyperspectral images based on adaptive band regrouping is proposed. Firstly, the affinity propagation clustering algorithm (AP) is chosen for band regrouping according to interband correlation. Then a linear prediction algorithm based on context prediction is applied to the hyperspectral images in different groups. Finally, the experimental results show that the proposed algorithm achieves performance gains of 1.12bpp over the conventional algorithm.

Original languageEnglish
Title of host publicationSatellite Data Compression, Communication, and Processing V
DOIs
StatePublished - 2009
EventSatellite Data Compression, Communication, and Processing V - San Diego, CA, United States
Duration: 4 Aug 20095 Aug 2009

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume7455
ISSN (Print)0277-786X

Conference

ConferenceSatellite Data Compression, Communication, and Processing V
Country/TerritoryUnited States
CitySan Diego, CA
Period4/08/095/08/09

Keywords

  • Affinity propagation clustering algorithm
  • Band regrouping
  • Hyperspectral image
  • Lossless compression

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