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.
Keywords
- Discrete Cosine Transform
- Independent Component Analysis
- Side Information
- Component Scalability
- Eigenvalue Decomposition
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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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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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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