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Proceedings Paper

Compression of multispectral AVIRIS images
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Paper Abstract

We have composed several lossy compression methods for multispectral images. These methods include the Self-Organizing Map (SOM), Principal Component Analysis (PCA) and the three-dimensional wavelet transform combined with traditional lossless coding methods. The two-dimensional DCT/JPEG, JPEG2000 and SPIHT compression methods were applied to eigenimages produced by the PCA. The information loss from the compression was measured with Signal-to-Noise-Ratio (SNR) and Peak-Signal-to-Noise ratio (PSNR). To get more illustrative error measures C-means clustering and Euclidean distance for spectral matching were used. The test image was an AVIRIS image with 224 bands and 512 lines in 614 columns. The PCA in the spectral dimension was the best method in terms of image quality and compression speed. If required, JPEG2000 or SPIHT can be applied in spatial dimensions to get better compression ratios.

Paper Details

Date Published: 2 August 2002
PDF: 12 pages
Proc. SPIE 4725, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery VIII, (2 August 2002); doi: 10.1117/12.478793
Show Author Affiliations
Arto Kaarna, Lappeenranta Univ. of Technology (Finland)
Pekka J. Toivanen, Lappeenranta Univ. of Technology (Finland)
Pekka Keranen, Lappeenranta Univ. of Technology (Finland)


Published in SPIE Proceedings Vol. 4725:
Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery VIII
Sylvia S. Shen; Paul E. Lewis, Editor(s)

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