Visualization, Band Ordering and Compression of Hyperspectral Images

Algorithms

Raffaele Pizzolante¹ 

[1] Dipartimento di Informatica, Università di Salerno, Fisciano (SA) 84084, Italy;

关键词: lossless compression;    image compression;    hyperspectral images;    band ordering;    remote sensing;    3D data;   

DOI  :  10.3390/a5010076

来源: mdpi

【 摘 要 】

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 [1,2,3,4,5,6,7]). 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.

【 授权许可】

CC BY   
© 2012 by the authors; licensee MDPI, Basel, Switzerland.