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Using Predictive and Differential Methods with K2-Raster Compact Data Structure for Hyperspectral Image Lossless Compression

Department of Information and Communications Engineering, Universitat Autònoma de Barcelona, Cerdanyola del Vallès, 08193 Barcelona, Spain
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This paper is an extended version of our paper published in the 6th ESA/CNES International Workshop on On-Board Payload Data Compression Proceedings.
Remote Sens. 2019, 11(21), 2461; https://doi.org/10.3390/rs11212461
Received: 31 August 2019 / Revised: 16 October 2019 / Accepted: 17 October 2019 / Published: 23 October 2019
(This article belongs to the Special Issue Real-Time Processing of Remotely-Sensed Imaging Data)
This paper proposes a lossless coder for real-time processing and compression of hyperspectral images. After applying either a predictor or a differential encoder to reduce the bit rate of an image by exploiting the close similarity in pixels between neighboring bands, it uses a compact data structure called k 2 -raster to further reduce the bit rate. The advantage of using such a data structure is its compactness, with a size that is comparable to that produced by some classical compression algorithms and yet still providing direct access to its content for query without any need for full decompression. Experiments show that using k 2 -raster alone already achieves much lower rates (up to 55% reduction), and with preprocessing, the rates are further reduced up to 64%. Finally, we provide experimental results that show that the predictor is able to produce higher rates reduction than differential encoding. View Full-Text
Keywords: compact data structure; quadtree; k2-tree; k2-raster; DACs; 3D-CALIC; M-CALIC; hyperspectral images compact data structure; quadtree; k2-tree; k2-raster; DACs; 3D-CALIC; M-CALIC; hyperspectral images
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Chow, K.; Tzamarias, D.E.O.; Blanes, I.; Serra-Sagristà, J. Using Predictive and Differential Methods with K2-Raster Compact Data Structure for Hyperspectral Image Lossless Compression . Remote Sens. 2019, 11, 2461.

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