Algorithms 2012, 5(1), 76-97; doi:10.3390/a5010076

Visualization, Band Ordering and Compression of Hyperspectral Images

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Received: 16 November 2011; in revised form: 18 January 2012 / Accepted: 30 January 2012 / Published: 20 February 2012
(This article belongs to the Special Issue Data Compression, Communication and Processing)
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.
Abstract: 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.
Keywords: lossless compression; image compression; hyperspectral images; band ordering; remote sensing; 3D data
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MDPI and ACS Style

Pizzolante, R.; Carpentieri, B. Visualization, Band Ordering and Compression of Hyperspectral Images. Algorithms 2012, 5, 76-97.

AMA Style

Pizzolante R, Carpentieri B. Visualization, Band Ordering and Compression of Hyperspectral Images. Algorithms. 2012; 5(1):76-97.

Chicago/Turabian Style

Pizzolante, Raffaele; Carpentieri, Bruno. 2012. "Visualization, Band Ordering and Compression of Hyperspectral Images." Algorithms 5, no. 1: 76-97.

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