Hyperspectral data compression using a Wiener filter predictor

Pierre V. Villeneuve; Scott G. Beaven; Alan D. Stocker

doi:10.1117/12.2024629

24 September 2013 Hyperspectral data compression using a Wiener filter predictor

Pierre V. Villeneuve, Scott G. Beaven, Alan D. Stocker

Proceedings Volume 8871, Satellite Data Compression, Communications, and Processing IX; 887102 (2013) https://doi.org/10.1117/12.2024629
Event: SPIE Optical Engineering + Applications, 2013, San Diego, California, United States

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 .

Citation Download Citation

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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