Optical Remote Sensing

Volume 3 of the series Augmented Vision and Reality pp 9-29


Hyperspectral Data Compression Tradeoff

  • Emmanuel ChristopheAffiliated withCentre for Remote Imaging, Sensing and Processing, National University of Singapore Email author 

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Hyperspectral data are a challenge for data compression. Several factors make the constraints particularly stringent and the challenge exciting. First is the size of the data: as a third dimension is added, the amount of data increases dramatically making the compression necessary at different steps of the processing chain. Also different properties are required at different stages of the processing chain with variable tradeoff. Second, the differences in spatial and spectral relation between values make the more traditional 3D compression algorithms obsolete. And finally, the high expectations from the scientists using hyperspectral data require the assurance that the compression will not degrade the data quality. All these aspects are investigated in the present chapter and the different possible tradeoffs are explored. In conclusion, we see that a number of challenges remain, of which the most important is to find an easier way to qualify the different algorithm proposals.