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.
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.