Using predictive and differential methods with K2-Raster compact data structure for hyperspectral image lossless compression

Kevin Chow*, Dion Eustathios Olivier Tzamarias, Ian Blanes, Joan Serra-Sagristà

*Corresponding author for this work

Research output: Contribution to journalArticleResearchpeer-review

1 Citation (Scopus)


This paper proposes a lossless coder for real-time processing and compression of hyperspectral images. After applying either a predictor or a differential encoder to reduce the bit rate of an image by exploiting the close similarity in pixels between neighboring bands, it uses a compact data structure called k2-raster to further reduce the bit rate. The advantage of using such a data structure is its compactness, with a size that is comparable to that produced by some classical compression algorithms and yet still providing direct access to its content for query without any need for full decompression. Experiments show that using k2-raster alone already achieves much lower rates (up to 55% reduction), and with preprocessing, the rates are further reduced up to 64%. Finally, we provide experimental results that show that the predictor is able to produce higher rates reduction than differential encoding.

Original languageAmerican English
Article number2461
JournalRemote Sensing
Issue number21
Publication statusPublished - 1 Nov 2019


  • 3D-CALIC
  • Compact data structure
  • DACs
  • Hyperspectral images
  • K-raster
  • K-tree
  • Quadtree

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