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13 March 2003 Spectral similarity measures for classification in lossy compression of hyperspectral images
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Several powerful lossy compression methods have been developed for hyperspectral images. However, it is difficult to determine sufficient quality for reconstructed hyperspectral images. We have measured the information loss from the lossy compression with Signal-to-Noise-Ratio (SNR) and Peak-Signal-to-Noise-Ratio (PSNR). To get more illustrative error measures unsupervised K-means clustering combined with spectral matching methods was used. Spectral matching methods include Euclidean distance, Spectral Similarity Value (SSV) and Spectral Angle Mapper (SAM). We used two AVIRIS radiance images, which were compressed with three different methods: the Self-Organizing Map (SOM), Principal Component Analysis (PCA) and three-dimensional wavelet transform combined with lossless BWT/Huffman encoding. The two-dimensional JPEG2000 compression method was applied to the eigenimages produced by the PCA. It was found that clustering combined with spectral matching is a good method to realize the image quality for many applications. The high classification accuracies have been achieved even at very high compression ratios. The SAM and the SSV are much more vulnerable for information loss caused by the lossy compression than the Euclidean distance. The results suggest that lossy compression is possible in many real-world segmentation applications. The PCA transform combined with JPEG2000 was the best compression method according to all error metrics.
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Pekka Keranen, Arto Kaarna, and Pekka J. Toivanen "Spectral similarity measures for classification in lossy compression of hyperspectral images", Proc. SPIE 4885, Image and Signal Processing for Remote Sensing VIII, (13 March 2003);

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