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Algorithms 2016, 9(1), 16; doi:10.3390/a9010016

Multiband and Lossless Compression of Hyperspectral Images

Dipartimento di Informatica, Università di Salerno, Fisciano (SA) 84084, Italy
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Academic Editor: Tatsuya Akutsu
Received: 28 December 2015 / Revised: 1 February 2016 / Accepted: 5 February 2016 / Published: 18 February 2016
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Abstract

Hyperspectral images are widely used in several real-life applications. In this paper, we investigate on the compression of hyperspectral images by considering different aspects, including the optimization of the computational complexity in order to allow implementations on limited hardware (i.e., hyperspectral sensors, etc.). We present an approach that relies on a three-dimensional predictive structure. Our predictive structure, 3D-MBLP, uses one or more previous bands as references to exploit the redundancies among the third dimension. The achieved results are comparable, and often better, with respect to the other state-of-art lossless compression techniques for hyperspectral images. View Full-Text
Keywords: hyperspectral images; lossless compression; low complexity; 3-D data hyperspectral images; lossless compression; low complexity; 3-D data
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Pizzolante, R.; Carpentieri, B. Multiband and Lossless Compression of Hyperspectral Images. Algorithms 2016, 9, 16.

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