Conclusions

Chapter

Abstract

Hyperspectral imaging has emerged as a very promising versatile signal processing technique in remote sensing image processing for a wide range of applications, from traditional remote sensing areas such as geology, forestry, agriculture, and environmental monitoring to new found areas such as medical imaging and food safety and inspection. In particular, its great potential in new applications is yet to explore. However, in order for a hyperspectral imaging sensor to be useful, software design and development is key to its success. This is similar to a scenario where no matter how expensive and luxury a car is, without gas to drive it around this car can only stay to be exhibited in a showroom and cannot go anywhere. Accordingly, what gas is to a car is the same as what software is to a sensor. So, for a sensor to do what it is designed for, algorithm design and development is core to realizing the sensors. The author’s first book, Hyperspectral Imaging: Spectral Techniques for Detection and Classification (Chang 2003) is the first work with such intention to address this issue by focusing on hyperspectral imaging algorithm design for spectral detection and classification. It is then followed by the author’s second book, Hyperspectral Data Processing: Algorithm Design and Analysis (Chang 2013), which expands algorithm design and development to cover various applications in hyperspectral image and signal processing. This book can be considered as a sequel to these two books with the main theme of processing hyperspectral imagery progressively in real time. Finally, this book is complemented by a forthcoming companion book by the author, Recursive Hyperspectral Sample and Band Processing.

Keywords

Remote Sensing 

References

  1. Bateson, C.A., G.P. Asner, and C.A. Wessman. 2000. Endmember bundles: A new approach to incorporating endmember variability into spectral mixture analysis. IEEE Transactions on Geoscience and Remote Sensing 38(2): 1083–1093.Google Scholar
  2. Boardman, J.W. 1994. Geometric mixture analysis of imaging spectrometry data. In International Geoscience and Remote Sensing Symposium (vol. 4, 2369–2371).Google Scholar
  3. Chang, C.-I 2003. Hyperspectral imaging: Techniques for spectral detection and classification. New York: Kluwer Academic/Plenum Publishers.Google Scholar
  4. Chang, C.-I 2013. Hyperspectral data processing: Algorithm design and analysis. New Jersey: Wiley.CrossRefMATHGoogle Scholar
  5. Chang, C.-I 2016. Recursive hyperspectral sample and band processing: Algorithm architecture and implementation. New York: Springer.Google Scholar
  6. Chang, C.-I, C.C. Wu, W. Liu, and Y.C. Ouyang. 2006. A growing method for simplex-based endmember extraction algorithms. IEEE Transactions on Geoscience and Remote Sensing 44(10): 2804–2819.CrossRefGoogle Scholar
  7. Chang, C.-I, W. Xiong and S.Y. Chen. 2016. Convex cone volume analysis for finding endmembers in hyperspectral imagery. International Journal of Computational Science and Engineering (to appear).Google Scholar
  8. Dennison, P.E., and D.A. Roberts. 2003. Endmember selection for multiple endmember spectral mixture analysis using endmember average RMSE. Remote Sensing of Environments 87: 123–135.Google Scholar
  9. Duda, R.O., and P.E. Hart. 1973. Pattern classification and scene Analysis. New Jersy: Wiley.Google Scholar
  10. Gao, C., and C.-I Chang. 2014. Recursive automatic target generation process for unsupervised hyperspectral target detection. In 2014 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Quebec Canada (July 13–18, 2014).Google Scholar
  11. Gao, C., S.Y. Chen, and C.-I Chang. 2014. Fisher’s ratio-based criterion for finding endmembers in hyperspectral imagery. In Satellite Data Compression, Communication and Processing X (ST146), SPIE International Symposium on SPIE Sensing Technology + Applications, Baltimore, MD (May 5–9, 2014).Google Scholar
  12. Gao, C., Y. Li, and C.-I Chang. 2015. Finding endmember classes in hyperspectral imagery. In Satellite Data Compression, Communication and Processing XI (ST127), SPIE International Symposium on SPIE Sensing Technology + Applications, Baltimore, MD (20–24 April, 2015).Google Scholar
  13. Gersho, A., and R.M. Gray. 1992. Vector quantization and signal compression. New York: Kluwer Academics Publishers.CrossRefMATHGoogle Scholar
  14. Heinz, D., and C.-I Chang. 2001. Fully constrained least squares linear mixture analysis for material quantification in hyperspectral imagery. IEEE Transactions on Geoscience and Remote Sensing 39(3): 529–545.Google Scholar
  15. Jin, J., B. Wang, and L. Zhang. 2010. A novel approach based on Fisher discriminant null space for decomposition of mixed pixels in hyperspectral imagery. IEEE Geoscience and Remote Sensing Letter 7(4): 699–703.Google Scholar
  16. Poor, H.V. 1994. An introduction to detection and estimation theory, 2nd ed. New York: Springer.CrossRefMATHGoogle Scholar
  17. Roberts, D.A., M. Gardner, R. Church, S.L. Ustin, G. Scheer, and R.O. Green. 1998. Mapping chaparral in the Santa Monica mountains using multiple endmember spectral mixture model. Remote Sensing of Environments 65: 267–279.Google Scholar
  18. Schowengerdt, R.A. 1997. Remote sensing: Models and methods for image processing (2nd ed.). Cambridge: Academic Press.   Google Scholar
  19. Somers, B., G.P. Asner, L. Tits, and P. Coppin. 2011. Endmember variability in spectral mixture analysis: A review. Remote Sensing Environment 115(7): 1603–1616.Google Scholar
  20. Song, C. 2005. Spectral mixture analysis for subpixel vegetation fractions in the urban environment: How to incorporate endmember variability? Remote Sensing Environment 95(2): 248–263.Google Scholar
  21. Winter, M.E. 1999a. Fast autonomous spectral endmember determination in hyperspectral data. In Proc. of 13th International Conference on Applied Geologic Remote Sensing, Vancouver, B.C., Canada (vol. II, 337–344).Google Scholar
  22. Winter, M.E. 1999b. N-finder: An algorithm for fast autonomous spectral endmember determination in hyperspectral data. In Image Spectrometry V, Proceedings of SPIE (vol. 3753, 266–277).Google Scholar
  23. Xiong, W., C.T. Tsai, C.W. Yang, and C.-I Chang. 2010. Convex cone-based endmember extraction for hyperspectral imagery. SPIE (vol. 7812), San Diego, CA, August 2–5, 2010.Google Scholar
  24. Zhang, J., B. Rivard, A. Sanchez-Azofeifa and K. Castro-Esau. 2006. Intra- and inter-class spectral variability of tropical tree species at La Selva Costa Rica: Implications for specicies indetification using HYDICE imagery. Remote Sensing Environment 105: 129–141.Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2016

Authors and Affiliations

  1. 1.BaltimoreUSA

Personalised recommendations