Abstract
Hyperspectral imagery (HSI) suffers from extremely large data volumes for storage, transmission, and processing.
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References
Chang CI, Du Q, Tu LG et al (1999) A joint band prioritization and band-deccorelation approach to band selection for hyperspectral image classification. IEEE Trans Geosci Remote Sens. 37:2631–2640
Davis GM, Nosratini A (1999) Wavelet-based image coding: an overview. Appl Comput Control Signals Circ 1999(1):6
Devijver PA, Kittler J (1982) Pattern recognition: a statistical approach. Prentice-Hall, Englewood Cliffs
Foerster H, Stüben K, Trottenberg U (1981) Nonstandard multigrid techniques using checkered relaxations and intermediate grids. In: Schultz M (ed) Elliptic problem solvers. Academic Press, New York, pp 285–300
Fry TW, Hauck S (2002) Hyperspectral image compression on reconfigurable platforms. The 10th annual IEEE symposium on field-programmable custom computing machines, 251–260
Gu YF, Zhang Y (2003) Unsupervised subspace linear spectral mixture analysis for hyperspectral images. Image Process 1:801–804
Hubert M, Rousseeuw PJ, Vanden BK (2005) ROBPCA: a new approach to robust principal component analysis. Technometrics 47(1):64–79
Hermes L, Buhmann JM (2000) Feature selection for support vector machines. Pattern Recog 2(3–7):712–715
Jelena K, Wim S (2000) Wavelet families of increasing order in arbitrary dimensions. IEEE Trans Image Process 9(3):480–496
Marin JA, BrpckhausJ, Schafer J (1999) A assessing band selection and image classification techniques on HYDICE hyperspectral data. IEEE Trans Syst Man Cybernetics 1:1067–1072
Pabitra M, Murthy CA, Sankar KP (2002) Unsupervised feature selection using feature similarity. IEEE Trans Pattern Anal Mach Intell 3:301–302
Suykens JAK, Brabanter JD, Lukas L, Vandewalle J (2002) Weighted least squares support vector machines: robustness and sparse approximation. Neurocomputing 48(1–4):85–105
Uytterhoeven G, Bultheel A (1998) The red-black wavelet transform. Proc IEEE Benelux Signal Process Symp 3:191–194
Vapnik VN (1995) The nature of statistical learning theory. Springer Press, New York
Wang Q, Guo L (2003) Multispectral image compression based on 1+2 dimension wavelet transform. J Photons 32(9):1126–1129
Webb A (1999) Statistical pattern recognition. Wiley, New York
Wim S (1995) The lifting scheme: a new philosophy in biorthogonal wavelet constructions. In: Proceeding of SPIE, wavelet applications in signal and image procession III (c): 68–79
Wim S (1997) The lifting scheme: a construction of second generation wavelets. SIAM Math Anal 29(2):511–546
Yan JW (2002) Digital image processing technology and image graphics basic tutorial. Sci Press, Beijing
Zhang LY (2005) The research of hyperspectral remote sensing image compression algorithm. Harbin engineering university master’s degree thesis
Zhang Y, Zhang JP (2001) Remote sensing spectrum (Hyperspectral) image processing technology. J Chin Image Graph 6(1):6–13
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© 2016 National Defense Industry Press, Beijing and Springer-Verlag Berlin Heidelberg
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Wang, L., Zhao, C. (2016). Dimensionality Reduction and Compression Technique of HSI. In: Hyperspectral Image Processing. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-47456-3_8
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DOI: https://doi.org/10.1007/978-3-662-47456-3_8
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