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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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Received: 16 November 2011; in revised form: 18 January 2012 / Accepted: 30 January 2012 / Published: 20 February 2012
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.
Pizzolante R, Carpentieri B. Visualization, Band Ordering and Compression of Hyperspectral Images. Algorithms. 2012; 5(1):76-97.
Pizzolante, Raffaele; Carpentieri, Bruno. 2012. "Visualization, Band Ordering and Compression of Hyperspectral Images." Algorithms 5, no. 1: 76-97.