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24 September 2013 Hyperspectral data compression using a Wiener filter predictor
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Abstract
The application of compression to hyperspectral image data is a significant technical challenge. A primary bottleneck in disseminating data products to the tactical user community is the limited communication bandwidth between the airborne sensor and the ground station receiver. This report summarizes the newly-developed “Z-Chrome” algorithm for lossless compression of hyperspectral image data. A Wiener filter prediction framework is used as a basis for modeling new image bands from already-encoded bands. The resulting residual errors are then compressed using available state-of-the-art lossless image compression functions. Compression performance is demonstrated using a large number of test data collected over a wide variety of scene content from six different airborne and spaceborne sensors .
© (2013) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Pierre V. Villeneuve, Scott G. Beaven, and Alan D. Stocker "Hyperspectral data compression using a Wiener filter predictor", Proc. SPIE 8871, Satellite Data Compression, Communications, and Processing IX, 887102 (24 September 2013); https://doi.org/10.1117/12.2024629
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