visualization, band ordering and compression of hyperspectral images可视化,带点和高光谱图像的压缩.pdfVIP

Algorithms 2012, 5, 76-97; doi:10.3390/a5010076 OPEN ACCESS algorithms ISSN 1999-4893 /journal/algorithms Article Visualization, Band Ordering and Compression of Hyperspectral Images Raffaele Pizzolante and Bruno Carpentieri * Dipartimento di Informatica, Università di Salerno, Fisciano (SA) 84084, Italy; E-Mail: raffaelepizz@hotmail.it * Author to whom correspondence should be addressed; E-Mail: bc@dia.unisa.it. 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 1. Introduction Hyperspectral remote sensing obtains inf