Efficient spatial-spectral compression of hyperspectral data

Author

Pickering, Mark R. ; Ryan, Michael J.

Author_Institution

Sch. of Electr. Eng., New South Wales Univ., Kensington, NSW, Australia

Volume

39

Issue

7

fYear

2001

fDate

7/1/2001 12:00:00 AM

Firstpage

1536

Lastpage

1539

Abstract

Mean-normalized vector quantization (M-NVQ) has been demonstrated to be the preferred technique for lossless compression of hyperspectral data. In this paper, a jointly optimized spatial M-NVQ/spectral DCT technique is shown to produce compression ratios significantly better than those obtained by the optimized spatial M-NVQ technique alone

Keywords

discrete cosine transforms; geophysical signal processing; geophysical techniques; image coding; multidimensional signal processing; terrain mapping; vector quantisation; IR; discrete cosine transform; geophysical measurement technique; hyperspectral data; hyperspectral remote sensing; image compression; infrared; land surface; lossless compression; mean-normalized vector quantization; remote sensing; spatial spectral method; spatial-spectral compression; terrain mapping; vector quantization; visible; Australia; Discrete cosine transforms; Distortion measurement; Entropy; Frequency; Hyperspectral imaging; Image coding; Loss measurement; Pixel; Vector quantization;

fLanguage

English

Journal_Title

Geoscience and Remote Sensing, IEEE Transactions on

Publisher

ieee

ISSN

0196-2892

Type

jour

DOI

10.1109/36.934084

Filename

934084