Open Access

Distributed Source Coding Techniques for Lossless Compression of Hyperspectral Images

  • Enrico Magli1Email author,
  • Mauro Barni2,
  • Andrea Abrardo2 and
  • Marco Grangetto1
EURASIP Journal on Advances in Signal Processing20072007:045493

Received: 10 February 2006

Accepted: 23 October 2006

Published: 15 January 2007


This paper deals with the application of distributed source coding (DSC) theory to remote sensing image compression. Although DSC exhibits a significant potential in many application fields, up till now the results obtained on real signals fall short of the theoretical bounds, and often impose additional system-level constraints. The objective of this paper is to assess the potential of DSC for lossless image compression carried out onboard a remote platform. We first provide a brief overview of DSC of correlated information sources. We then focus on onboard lossless image compression, and apply DSC techniques in order to reduce the complexity of the onboard encoder, at the expense of the decoder's, by exploiting the correlation of different bands of a hyperspectral dataset. Specifically, we propose two different compression schemes, one based on powerful binary error-correcting codes employed as source codes, and one based on simpler multilevel coset codes. The performance of both schemes is evaluated on a few AVIRIS scenes, and is compared with other state-of-the-art 2D and 3D coders. Both schemes turn out to achieve competitive compression performance, and one of them also has reduced complexity. Based on these results, we highlight the main issues that are still to be solved to further improve the performance of DSC-based remote sensing systems.


Authors’ Affiliations

Center for Multimedia Radio Communications (CERCOM), Department of Electronics, Politecnico di Torino
Dipartimento di Ingegneria dell'Informazione, Università di Siena


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© Enrico Magli et al. 2007

This article is published under license to BioMed Central Ltd. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.