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J. Imaging 2018, 4(12), 142;

Efficient Lossless Compression of Multitemporal Hyperspectral Image Data

Bank of America Corporation, New York, NY 10020, USA
Department of Electrical and Computer Engineering, University of Alabama in Huntsville, Huntsville, AL 35899, USA
Note: This paper is continuation of the first author’s PhD work, which is not associated with his current affiliation.
Author to whom correspondence should be addressed.
Received: 14 November 2018 / Accepted: 27 November 2018 / Published: 2 December 2018
(This article belongs to the Special Issue The Future of Hyperspectral Imaging)
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Hyperspectral imaging (HSI) technology has been used for various remote sensing applications due to its excellent capability of monitoring regions-of-interest over a period of time. However, the large data volume of four-dimensional multitemporal hyperspectral imagery demands massive data compression techniques. While conventional 3D hyperspectral data compression methods exploit only spatial and spectral correlations, we propose a simple yet effective predictive lossless compression algorithm that can achieve significant gains on compression efficiency, by also taking into account temporal correlations inherent in the multitemporal data. We present an information theoretic analysis to estimate potential compression performance gain with varying configurations of context vectors. Extensive simulation results demonstrate the effectiveness of the proposed algorithm. We also provide in-depth discussions on how to construct the context vectors in the prediction model for both multitemporal HSI and conventional 3D HSI data. View Full-Text
Keywords: lossless compression; multitemporal hyperspectral images; information theoretic analysis; predictive coding lossless compression; multitemporal hyperspectral images; information theoretic analysis; predictive coding

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Shen, H.; Jiang, Z.; Pan, W.D. Efficient Lossless Compression of Multitemporal Hyperspectral Image Data. J. Imaging 2018, 4, 142.

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