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2 August 2002 Compression of multispectral AVIRIS images
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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.
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Arto Kaarna, Pekka J. Toivanen, and Pekka Keranen "Compression of multispectral AVIRIS images", Proc. SPIE 4725, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery VIII, (2 August 2002);

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