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Proceedings Paper

VST-based lossy compression of hyperspectral data for new generation sensors
Author(s): Alexander N. Zemliachenko; Ruslan A. Kozhemiakin; Mykhail L. Uss; Sergey K. Abramov; Vladimir V. Lukin; Benoit Vozel; Kacem Chehdi
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Paper Abstract

This paper addresses lossy compression of hyperspectral images acquired by sensors of new generation for which signaldependent component of the noise is prevailing compared to the noise-independent component. First, for sub-band (component-wise) compression, it is shown that there can exist an optimal operation point (OOP) for which MSE between compressed and noise-free image is minimal, i.e., maximal noise filtering effect is observed. This OOP can be observed for two approaches to lossy compression where the first one presumes direct application of a coder to original data and the second approach deals with applying direct and inverse variance stabilizing transform (VST). Second, it is demonstrated that the second approach is preferable since it usually provides slightly smaller MSE and slightly larger compression ratio (CR) in OOP. One more advantage of the second approach is that the coder parameter that controls CR can be set fixed for all sub-band images. Moreover, CR can be considerably (approximately twice) increased if sub-band images after VST are grouped and lossy compression is applied to a first sub-band image in a group and to “difference” images obtained for this group. The proposed approach is tested for Hyperion hyperspectral images and shown to provide CR about 15 for data compression in the neighborhood of OOP.

Paper Details

Date Published: 17 October 2013
PDF: 12 pages
Proc. SPIE 8892, Image and Signal Processing for Remote Sensing XIX, 88920L (17 October 2013); doi: 10.1117/12.2028415
Show Author Affiliations
Alexander N. Zemliachenko, National Aerospace Univ. (Ukraine)
Ruslan A. Kozhemiakin, National Aerospace Univ. (Ukraine)
Mykhail L. Uss, National Aerospace Univ. (Ukraine)
Sergey K. Abramov, National Aerospace Univ. (Ukraine)
Vladimir V. Lukin, National Aerospace Univ. (Ukraine)
Benoit Vozel, IETR, UMR CNRS, Univ. de Rennes 1 (France)
Kacem Chehdi, IETR, UMR CNRS, Univ. de Rennes 1 (France)

Published in SPIE Proceedings Vol. 8892:
Image and Signal Processing for Remote Sensing XIX
Lorenzo Bruzzone, Editor(s)

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