Lossless hyperspectral-image compression using context-based conditional average

Hongqiang Wang, S. Derin Babacan, Khalid Sayood

Research output: Contribution to journalArticlepeer-review

59 Scopus citations


In this paper, a new algorithm for lossless compression of hyperspectral images is proposed. The spectral redundancy in hyperspectral images is exploited using a context-match method driven by the correlation between adjacent bands. This method is suitable for hyperspectral images in the band-sequential format. Moreover, this method compares favorably with the recent proposed lossless compression algorithms in terms of compression, with a lower complexity.

Original languageEnglish (US)
Pages (from-to)4187-4193
Number of pages7
JournalIEEE Transactions on Geoscience and Remote Sensing
Issue number12
StatePublished - Dec 2007


  • Conditional average
  • Context coding
  • Correlation
  • Entropy code
  • Golomb-rice code
  • Hyperspectral image
  • Image coding

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Earth and Planetary Sciences(all)


Dive into the research topics of 'Lossless hyperspectral-image compression using context-based conditional average'. Together they form a unique fingerprint.

Cite this