Transform coding techniques for lossy hyperspectral data compression

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING (SUBMITTED DEC. 2005) 27 Fig. 16. Classification result respectively on the original image (left), on the compressed image with full-complexity KLT at 1 bpp (center), on the compressed image with low-complexity KLT (ρ =0.01) at 1 bpp (right). The number of clusters is equal to 3. The best spectral transform has turned out to be the KLT. In order to make this transform computationally feasible, we have proposed a low-complexity version with comparable performance. The degree of computational saving and the related performance loss can be tuned to the specific needs of each application. The low-complexity KLT, along with a hybrid wavelet-based scheme, have been integrated into a JPEG 2000 Part 2 compliant scheme. Tests have been carried out on AVIRIS data, and comparisons have been performed with respect to 3D-SPIHT and SPECK. The proposed KLT-based scheme achieves significant performance gains with respect to the hybrid schemes and 3D-SPIHT; it outperforms SPECK by 5 to 10 dB in PSNR. An end-to-end complexity reduction of about three times can be achieved using the low-complexity KLT, with a minor performance loss (about 0.5 dB). This transform is only about 40% more complex than 3D wavelets, but has significantly better performance. A quality assessment of compressed images has also been carried out by evaluating the effects of several lossy compression schemes on the results of SAM classification. It turns out that, for this application, PSNR is a good indicator of classification performance, so that the proposed scheme is still the highest-performance one by a large margin. REFERENCES [1] D.S. Taubman and M.W. Marcellin, JPEG2000: Image Compression Fundamentals, Standards, and Practice, Kluwer, 2001. [2] S. Lim, K. Sohn, and C. Lee, “Compression for hyperspectral images using three dimensional wavelet transform,” in Proc. of IGARSS - IEEE International Geoscience and Remote Sensing Symposium, Sydney, Australia, 2001. [3] Y. Tseng, H. Shih, and P. Hsu, “Hyperspectral image compression using three-dimensional wavelet transformation,” in Proceedings of the the 21st Asian Conference on Remote Sensing (ACRS), Taipei, Taiwan, 2000.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING (SUBMITTED DEC. 2005) 28 [4] A. Kaarna and J. Parkkinen, “Comparison of compression methods for multispectral images,” in Proc. of NORSIG - Nordic Signal Processing Symposium, Kolmarden, Sweden, 2000, vol. 2, pp. 251–254. [5] G.P. Abousleman, M.W. Marcellin, and B.R. Hunt, “Compression of hyperspectral imagery using the 3-D DCT and hybrid DPCM-DCT,” IEEE Transactions on Geoscience and Remote Sensing, vol. 33, no. 1, pp. 26–34, Jan. 1995. [6] D. Markman and D. Malah, “Hyperspectral image coding using 3D transforms,” in Proc. of ICIP - IEEE International Conference on Image Processing, Thessaloniki, Greece, 2001. [7] M.D. Pal, C.M. Brislawn, and S.R. Brumby, “Feature extraction from hyperspectral images compressed using the JPEG-2000 standard,” in Proc. of SSIAI - IEEE Southwest Symposium on Image Analysis and Interpretation, Santa Fe, New Mexico, 2002. [8] M.D. Pal and C.M. Brislawn S.P. Brumby, “Feature extraction form hyperspectral images compressed using the JPEG-2000 standard,” in Proc. of SSIAI Southwest Symposium on Image Analysis and Interpretation, Santa Fe, New Mexico, 2002. [9] S. Lim, K.H. Sohn, and C. Lee, “Principal component analysis for compression of hyperspectral images,” in Proc. of IGARSS - IEEE International Geoscience and Remote Sensing Symposium, Sydney, Australia, 2001. [10] X. Tang, C. Sungdae, and W.A. Pearlman, “3D set partitioning coding methods in hyperspectral image compression,” in Proc. of ICIP - IEEE International Conference on Image Processing, Barcelona, Spain, 2003. [11] X. Tang and W.A. Pearlman, “Three-dimensional wavelet-based compression of hyperspectral images,” in Hyperspectral Data Compression. Kluwer Academic Publishers, 2005. [12] J.A. Sagri, A.G. Tescher, and J.T. Reagan, “Practical transform coding of multispectral imagery,” IEEE Signal Processing Magazine, pp. 32–43, Jan. 1995. [13] P.L. Dragotti, G. Poggi, and A.R.P. Ragozini, “Compression of multispectral images by three-dimensional SPIHT algorithm,” IEEE Transactions on Geoscience and Remote Sensing, vol. 38, no. 1, pp. 416–428, Jan. 2000. [14] L. Chang, C. Cheng, and T. Chen, “An efficient adaptive KLT for multispectral image compression,” in Proceedings of 4th IEEE Southwest Symposium on Image Analysis and Interpretation, Austin, TX, 2000. [15] P. Hao and Q. Shi, “Reversible integer KLT for progressive-to-lossless compression of multiple component images,” in Proc of IEEE International Conference on Image Processing, 2003, Barcelona, Spain, 2003. [16] B. Penna, T.Tillo, E. Magli, and G. Olmo, “Progressive 3D coding of hyperspectral images based on JPEG 2000,” IEEE Geoscience and Remote Sensing Letters, to appear Jan. 2006. [17] E. Christophe, D. Léger, and C. Mailhes, “Quality criteria benchmark for hyperspectral imagery,” IEEE Transactions on Geoscience and Remote Sensing, vol. 43, no. 9, pp. 2103–2114, Sept. 2005. [18] V. Guralnik and G. Karypis, “A scalable algorithm for clustering protein sequences,” in Workshop on Data Mining in Bioinformatics, 2001. [19] JPEG 2000 Part 2 - Extensions, Document ISO/IEC 15444-2. [20] M. Vetterli and J. Kovacevic, Wavelet and Subband Coding, Pretince Hall, 1995. [21] R.R. Coifman and M.V. Wickerhauser, “Entropy-based algorithms for best basis selection,” IEEE Transactions on Information Theory, vol. 38, no. 2, pp. 713–718, Mar. 1992. [22] K. Ramchandran and M. Vetterli, “Best wavelet packet bases in a rate-distortion sense,” IEEE Transactions on Image Processing, vol. 2, no. 2, pp. 160–175, Apr. 1993. [23] V.K. Goyal, “Theoretical foundations of transform coding,” IEEE Signal Processing Magazine, vol. 18, no. 5, pp. 9–21, 2001. [24] I.S. Dhillon, ANewO(N 2 ) Algorithm for the Symmetric Tridiagonal Eigenvalue/Eigenvector Problem, Ph.D. Thesis, University of California, Berkeley, 1997.
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