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Visualization, Band Ordering and Compression of Hyperspectral Images

Dipartimento di Informatica, Università di Salerno, Fisciano (SA) 84084, Italy
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Algorithms 2012, 5(1), 76-97; https://doi.org/10.3390/a5010076
Received: 16 November 2011 / Revised: 18 January 2012 / Accepted: 30 January 2012 / Published: 20 February 2012
(This article belongs to the Special Issue Data Compression, Communication and Processing)
Air-borne and space-borne acquired hyperspectral images are used to recognize objects and to classify materials on the surface of the earth. The state of the art compressor for lossless compression of hyperspectral images is the Spectral oriented Least SQuares (SLSQ) compressor (see [1–7]). In this paper we discuss hyperspectral image compression: we show how to visualize each band of a hyperspectral image and how this visualization suggests that an appropriate band ordering can lead to improvements in the compression process. In particular, we consider two important distance measures for band ordering: Pearson’s Correlation and Bhattacharyya distance, and report on experimental results achieved by a Java-based implementation of SLSQ. View Full-Text
Keywords: lossless compression; image compression; hyperspectral images; band ordering; remote sensing; 3D data lossless compression; image compression; hyperspectral images; band ordering; remote sensing; 3D data
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Pizzolante, R.; Carpentieri, B. Visualization, Band Ordering and Compression of Hyperspectral Images. Algorithms 2012, 5, 76-97.

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