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Satellite Data Compression pp 215–232Cite as

Divide-and-Conquer Decorrelation for Hyperspectral Data Compression

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Abstract

Recent advances in the development of modern satellite sensors have increased the need for image coding, because of the huge volume of such collected data. It is well-known that the Karhunen-Loêve transform provides the best spectral decorrelation. However, it entails some drawbacks like high computational cost, high memory requirements, its lack of component scalability, and its difficult practical implementation. In this contributed chapter we revise some of the recent proposals that have been published to mitigate some of these drawbacks, in particular, those proposals based on a divide-and-conquer decorrelation strategy. In addition, we provide a comparison among the coding performance, the computational cost, and the component scalability of these different strategies, for lossy, for progressive lossy-to-lossless, and for lossless remote-sensing image coding.

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Acknowledgements

This work was supported in part by the Spanish Government, by the Catalan Government, and by FEDER under grants TIN2009-14426-C02-01, TIN2009-05737-E/TIN, SGR2009-1224, and FPU2008. Computational resources used in this work were partially provided by the Oliba Project of the Universitat Autònoma de Barcelona. This work was also supported by the Belgian Government via the Fund for Scientific Research Flanders (postdoctoral fellowship Peter Schelkens). The authors would like to thank NASA and USGS for providing the Hyperion images.

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Authors and Affiliations

  1. Universitat Autònoma de Barcelona, E-08290, Cerdanyola del Vallès, Barcelona, Spain

    Ian Blanes & Joan Serra-Sagristà

  2. Department of Electronics and Informatics, Vrije Universiteit Brussel, Pleinlaan 2, B-1050, Brussels, Belgium

    Peter Schelkens

  3. Interdisciplinary Institute for Broadband Technology, Gaston Crommenlaan 8, b102, B-9050, Ghent, Belgium

    Peter Schelkens

Authors
  1. Ian Blanes
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  2. Joan Serra-Sagristà
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  3. Peter Schelkens
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Correspondence to Ian Blanes .

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Editors and Affiliations

  1. , Space Science and Engineering Center, University of Wisconsin - Madison, Madison, 53706, Wisconsin, USA

    Bormin Huang

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Blanes, I., Serra-Sagristà, J., Schelkens, P. (2012). Divide-and-Conquer Decorrelation for Hyperspectral Data Compression. In: Huang, B. (eds) Satellite Data Compression. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-1183-3_10

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  • DOI: https://doi.org/10.1007/978-1-4614-1183-3_10

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  • Publisher Name: Springer, New York, NY

  • Print ISBN: 978-1-4614-1182-6

  • Online ISBN: 978-1-4614-1183-3

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