Advertisement

Dimensionality Reduction and Compression Technique of HSI

  • Liguo WangEmail author
  • Chunhui Zhao
Chapter
  • 2.3k Downloads

Abstract

Hyperspectral imagery (HSI) suffers from extremely large data volumes for storage, transmission, and processing.

Keywords

Hyperspectral Image Vector Quantization Code Word Hyperspectral Data Training Vector 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 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. 37:2631–2640CrossRefGoogle Scholar
  2. Davis GM, Nosratini A (1999) Wavelet-based image coding: an overview. Appl Comput Control Signals Circ 1999(1):6Google Scholar
  3. Devijver PA, Kittler J (1982) Pattern recognition: a statistical approach. Prentice-Hall, Englewood CliffsGoogle Scholar
  4. 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 285–300Google Scholar
  5. Fry TW, Hauck S (2002) Hyperspectral image compression on reconfigurable platforms. The 10th annual IEEE symposium on field-programmable custom computing machines, 251–260Google Scholar
  6. Gu YF, Zhang Y (2003) Unsupervised subspace linear spectral mixture analysis for hyperspectral images. Image Process 1:801–804Google Scholar
  7. Hubert M, Rousseeuw PJ, Vanden BK (2005) ROBPCA: a new approach to robust principal component analysis. Technometrics 47(1):64–79MathSciNetCrossRefGoogle Scholar
  8. Hermes L, Buhmann JM (2000) Feature selection for support vector machines. Pattern Recog 2(3–7):712–715Google Scholar
  9. Jelena K, Wim S (2000) Wavelet families of increasing order in arbitrary dimensions. IEEE Trans Image Process 9(3):480–496CrossRefGoogle Scholar
  10. Marin JA, BrpckhausJ, Schafer J (1999) A assessing band selection and image classification techniques on HYDICE hyperspectral data. IEEE Trans Syst Man Cybernetics 1:1067–1072Google Scholar
  11. Pabitra M, Murthy CA, Sankar KP (2002) Unsupervised feature selection using feature similarity. IEEE Trans Pattern Anal Mach Intell 3:301–302Google Scholar
  12. Suykens JAK, Brabanter JD, Lukas L, Vandewalle J (2002) Weighted least squares support vector machines: robustness and sparse approximation. Neurocomputing 48(1–4):85–105CrossRefGoogle Scholar
  13. Uytterhoeven G, Bultheel A (1998) The red-black wavelet transform. Proc IEEE Benelux Signal Process Symp 3:191–194Google Scholar
  14. Vapnik VN (1995) The nature of statistical learning theory. Springer Press, New YorkCrossRefGoogle Scholar
  15. Wang Q, Guo L (2003) Multispectral image compression based on 1+2 dimension wavelet transform. J Photons 32(9):1126–1129Google Scholar
  16. Webb A (1999) Statistical pattern recognition. Wiley, New YorkGoogle Scholar
  17. 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): 68–79Google Scholar
  18. Wim S (1997) The lifting scheme: a construction of second generation wavelets. SIAM Math Anal 29(2):511–546Google Scholar
  19. Yan JW (2002) Digital image processing technology and image graphics basic tutorial. Sci Press, BeijingGoogle Scholar
  20. Zhang LY (2005) The research of hyperspectral remote sensing image compression algorithm. Harbin engineering university master’s degree thesisGoogle Scholar
  21. Zhang Y, Zhang JP (2001) Remote sensing spectrum (Hyperspectral) image processing technology. J Chin Image Graph 6(1):6–13Google Scholar

Copyright information

© National Defense Industry Press, Beijing and Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  1. 1.Harbin Engineering UniversityHarbinChina

Personalised recommendations