6. Conclusions
This chapter investigates the applicability of direct application of 3D compression techniques to hyperspectral imagery and develops PCA-based spectral/spatial compression techniques in conjunction with the virtual dimensionality (VD) for hyperspectral image compression where the VD is used to estimate number of principal components required to be preserved. In particular, we conduct computer simulations based on a synthetic image and real image experiments to demonstrate that simple PCA-based spectral/spatial lossy compression techniques can perform at least as well as 3D lossy compression techniques in applications such as mixed pixel classification and quantification. This interesting finding provides evidence that PCA-based spectral/spatial compression can be as competitive as the 3D compression for hyperspectral image compression. Additionally, this chapter also further demonstrates that the number of PCs required to be preserved by lossy compression is crucial and the proposed VD provides a much better estimate than the commonly used criterion determined by the sum of largest eigenvalues. For more details we refer to [31].
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References
V.D. Vaughn and T.S. Wilkinson, “System considerations for multspectral image compression designs,” IEEE Signal Processing Magazine, pp. 19–31, January 1995.
J.A. Saghri, A.G. Tescher and J.T. Reagan, “Practical transform coding of multispectral imagery,” IEEE Signal Processing Magazine, pp. 32–43, January 1995.
J.A. Saghri and A.G. Tescher, “Near-lossless handwidth compression for raiometric data,” Optical Engineering, vol. 30, no. 7, pp. 934–939, July 1991.
J.A. Saghri, A.G. Tescher and A. Boujarwah, “Spectral-signature-preserving compression of multispectral data,” Optical Engineering, vol. 38, no. 12, pp. 2081–2088, December 1999.
G.P. Abousleman, E. Gifford and B.R. Hunt, “Enhancement and compression techniques for hyperspectral data,” Optical Engineering, vol. 33, no. 8, pp. 2562–2571, August 1994.
G.P. Abousleman, M.W. Marcellin and B.R. Hunt, “Hyperspectral image compression using entropy-constrained predictive trellis coded quantization,” IEEE Trans. on Image Processing, vol.6, no. 4, pp. 566–573, April 1994.
G.P. Abousleman, M.W. Marcellin and B.R. Hunt, “Compression of hyperspectral imagery using the 3-D DCT and hybrid DPCM/DCT,” IEEE Trans. on Geoscience and Remote Sensing, vol.33, no. 1, pp. 26–34, April 1994.
S.-E. Qian, A.B. Hollinger, D. Williams and D. Manak, “Fast three-dimensional data compression of hyperspectral imagery using vector quantization with spectral-feature-based binary coding,” Optical Engineering, vol. 35, no. 7, pp. 3242–3249, November 1996.
R.O. Duda and R.E. Hart, Pattern Classification and Scene Analysis, John Wiley & Sons, New York, 1973.
J.A. Richards, Remote Sensing Digital Image Analysis, 2nd ed. Springer-Verlag. 1993.
A.A. Green, M. Berman, P. Switzer and M.D. Craig, “A transformation for ordering multispectral data in terms of image quality with implications for noise removal,” IEEE Trans. on Geoscience and Remote Sensing, vol. 26, no. 1, pp. 65–74, January 1988.
J.B. Lee, A.S. Woodyatt and M. Berman, “Enhancement of high spectral resolution remote sensing data by a noise-adjusted principal components transform,” IEEE Trans. on Geoscience and Remote Sensing, vol. 28, no. 3, pp. 295–304, May 1990.
C.-I Chang, Q. Du, T.S. Sun and M.L.G. Althouse, “A joint band prioritization and band decorrelation approach to band selection for hyperspectral image classification,” IEEE Trans. on Geoscience and Remote Sensing, vol. 37, no. 6, pp. 2631–2641, November 1999.
B.-J. Kim, Z. Xiong, and W.A. Pearlman, “Low bit-rate scalable video coding with 3-D set partitioning in hierarchical trees (3-D SPIHT),” IEEE Transactions on Circuits and Systems for Video Technology, vol. 10, no. 8, pp.1374–1387, December 2000.
A. Said and W.A. Pearlman, “A new, fast, and efficient image codec based on set partitioning in hierarchical trees,” IEEE Trans. on Circuits and systems for Video Technology, vol. 6, no. 3, pp. 243–350, June 1996.
D. Taubman, “High performance scalable image compression with EBCOT”, IEEE Trans. Image Proc., 9, 1158–1170
D. S. Taubman and M. W. Marcellin, JPEG2000: Image Compression Fundamentals, Standard and Practice. Boston, MA: Kluwer, 2002.
ISO, Information Technology—JPEG 2000 Image Coding System-Part 1: Core Coding System, ISO, Geneva, Switzerland, 2000.
ISO, Information Technology—JPEG 2000 Image Coding System-Part 2: Extensions; Final Committee Draft, ISO, Geneva, Switzerland, Dec. 2000.
C.-I Chang and Q. Du, “Estimation of number of spectrally distinct signal sources in hyperspectral imagery,” IEEE Trans. on Geoscience and Remote Sensing, vol. 42, no. 3, pp. 608–619, March 2004.
C.-I Chang, Hyperspectral Imaging: Techniques for Spectral Detection and Classification, Kluwer Academic/Plenum Publishers, New York, N.Y., 2003
J.C. Harsanyi, W. Farrand and C.-I Chang, “Detection of subpixel spectral signatures in hyperspectral image sequences,” Annual Meeting, Proceedings of American Society of Photogrammetry & Remote Sensing, Reno, pp. 236–247, 1994.
T.W. Anderson, Multivariate Analysis, Academic Press, 2nd ed., 1984.
C.-I Chang, Q. Du, T.S. Sun and M.L.G. Althouse, “A joint band prioritization and band decorrelation approach to band selection for hyperspectral image classification,” IEEE Trans. on Geoscience and Remote Sensing, vol. 37, no. 6, pp. 2631–2641, November 1999.
S. S. Shen and B. S. Beard, “Effects of hyperspectral compression on non-literal exploitation,” Proceedings of SPIE, vol. 3438, pp. 191–199, 1998.
Arto Kaarna, Pekka J. Toivanen, Pekka Keranen, “Compression of multispectral AVIRIS images,” Proceedings of SPIE, vol. 4725, pp.588–599, 2002
J.E. Fowler, “QccPack: An open-source software library for quantization, compression, and coding,” in Applications of Digital Image Processing XXIII, vol. 4115, pp.249–301, San Diego, CA, 2000.
Kakadu software: A Comprehensive Framework for JPEG2000, www.Kakadusoftware.com, Implementation of JPEG2000.
J.C. Harsanyi and C.-I Chang, “Hyperspectral image classification and dimensionality reduction: an orthogonal subspace projection,” IEEE Trans on Geoscience and Remote Sensing, vol. 32, no. 4, pp. 779–785, July 1994.
D. Heinz and C.-I Chang, “Fully constrained least squares linear mixture analysis for material quantification in hyperspectral imagery,” IEEE Trans. on Geoscience and Remote Sensing, vol. 39, no. 3, pp. 529–545, March 2001.
Bharath Ramakrishna, Principal Components Analysis (PCA)-Based Spectral/Spatial Hyperspectral Image Compression, MS. Thesis, Department of Computer Science and Electrical Engineering, University of Maryland, Baltimore County, Baltimore, MD, 2004.
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Ramakrishna, B., Plaza, A.J., Chang, CI., Ren, H., Du, Q., Chang, CC. (2006). Spectral/Spatial Hyperspectral Image Compression. In: Motta, G., Rizzo, F., Storer, J.A. (eds) Hyperspectral Data Compression. Springer, Boston, MA. https://doi.org/10.1007/0-387-28600-4_11
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