Overview and Introduction



Hyperspectral imaging has become an emerging technique in remote sensing and has also successfully found its way into many other applications such as medical imaging, medical care, health care, and food industries for grading, safety, and inspection. Its benefits and advantages come from its use of as many as hundreds of spectral bands with very high spatial/spectral resolution. However, it also pays a heavy price for the excessive data volumes needing to be processed. For example, in satellite communication this is a very challenging issue because of long distance transmission and limited available bandwidths as well as data storage. Also, in many real-world applications, real-time processing is important because decision making must be achieved on a timely basis. Despite the fact that real-time processing has been widely studied in recent years, it is unfortunate that most algorithms claiming to be real-time are actually not for the following reasons. First, theoretically speaking, there are no such real-time processes in practice because computer processing time is always required and causes time delay. Second, a real-time process must be causal in the sense that no data sample vectors beyond the current being processed data sample vector should be allowed to be included in the data processing. Third, a real-time process should take advantage of its processed information and only process so-called innovations information which is not available at the time the data processing takes place. Finally, many real-time processing algorithms currently being used assume that the data are collected after data acquisition and then process the collected data in a post-real-time fashion. So, technically speaking, these algorithms are not true real-time processes because they cannot be implemented in real time while the data are being collected at the same time. Accordingly, these algorithms cannot be used for real-time data communication and transmission. In recent applications hyperspectral imaging has the capability of finding targets that are generally not known by prior knowledge or identified by visual inspection, such as moving objects or instantaneous objects, which can only appear for a short time and may not reappear after they vanish. In this case, detecting these targets on a timely basis must be immediate and target detection must be carried out in a real-time fashion even when data are being collected during data acquisition. Unfortunately, many currently developed real-time processing algorithms generally do not meet these criteria and cannot be used for this purpose. This book takes up this task and is devoted to design and development of real-time processing algorithms for hyperspectral data processing from a perspective of Progressive HyperSpectral Imaging (PHSI).


Field Programmable Gate Array Anomaly Detection Image Scene Pixel Vector Constrained Energy Minimization 
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.


  1. Basedow, R., P. Silverglate, W. Rappoport, R. Rockwell, D. Rosenberg, K. Shu, R. Whittlesey, and E. Zalewski. 1992. The HYDICE instrument design. In Proceedings of international symposium on spectral sensing research, vol. 1, 430–445. 1992.Google Scholar
  2. Chang, C.-I, and C. Brumbley. 1999. A Kalman filtering approach to multispectral image classification and detection of changes in signature abundance. IEEE Transactions on Geoscience and Remote Sensing 37(1): 257–268, January 1999.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, 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, October 2006.Google Scholar
  5. Chang, C.-I 2013. Hyperspectral data processing: Algorithm design and analysis. New Jersey: Wiley.Google Scholar
  6. Chang, C.-I 2016. Recursive hyperspectral sample and band processing: Algorithm architecture and implementation. New York: Springer.Google Scholar
  7. Cheng, Y. 1993. Multistage pulse code modulation (MPCM), MS thesis, Department of Electrical Engineering, University of Maryland, Baltimore County, Baltimore, MD, 1993.Google Scholar
  8. Dempster, A.P., N.M. Laird, and D.B. Rubin. 1977. Maximum likelihood from in complete data via the EM algorithm. J. Royal Statistical Society. B 39(1): 1–38.Google Scholar
  9. Duda, R.O. and P.E. Hart. 1973. Pattern classification and scene analysis. New York: John Wiley & Sons.Google Scholar
  10. Gelb, A. 1974. Ed. Applied optimal estimation. Cambridge: MIT Press.Google Scholar
  11. Gersho, A., and R.M. Gray. 1992. Vector quantization and signal compression. New York: Kluwer Academics Publishers. 1992.CrossRefMATHGoogle Scholar
  12. Gonzalez, R.C., and R.E. Woods. 2008. Digital image processing, 3rd ed. Upper Saddle River: Prentice-Hall.Google Scholar
  13. Harsanyi, J.C. 1993. Detection and classification of subpixel spectral signatures in hyperspectral image sequences, Department of Electrical Engineering, University of Maryland, Baltimore County, MD, August 1993.Google Scholar
  14. Harsanyi, J.C., and C.-I Chang. 1994. Hyperspectral image classification and dimensionality reduction: An orthogonal subspace projection approach. IEEE Transactions on Geoscience and Remote Sensing 32(4): 779–785, July, 1994.Google Scholar
  15. Haung, B. ed. 2011. Satellite data compression. New York: Springer.Google Scholar
  16. Kailath, T. 1968. An innovations approach to least squares estimation. Part I: Linear filtering in adpative white noise. IEEE Trans. Automatic Control. 13(6): 646–655.Google Scholar
  17. Plaza, A., and C.-I Chang. ed. 2007a. High performance computing in remote sensing. Boca Raton: CRC Press.Google Scholar
  18. Plaza, A., and C.-I Chang. 2007b. Specific issues about high-performance computing in remote sensing, non-literal analysis versus image-based processing, Chapter 1, High-performance computing in remote sensing, edited by A. Plaza and C.-I Chang, CRC Press, 2007.Google Scholar
  19. Plaza, A., and C.-I Chang. 2007c. Clusters versus FPGAs for real-time processing of hyperspectral imagery. International Journal of High Performance Computing Applications, December 2007.Google Scholar
  20. Poor, H.V. 1994. An introduction to detection and estimation theory, 2nd ed. New York: Springer-Verlag.CrossRefMATHGoogle Scholar
  21. Reed, I.S., and X. Yu. 1990. Adaptive multiple-band CFAR detection of an optical pattern with unknown spectral distribution. IEEE Transactions on Acoustic, Speech and Signal Process 38(10): 1760–1770.Google Scholar
  22. Ren, H., and C.-I Chang. 2003. Automatic spectral target recognition in hyperspectral imagery. IEEE Transactions on Aerospace and Electronic Systems 39(4): 1232–1249, October 2003.Google Scholar
  23. Schowengerdt, R.A. 1997. Remote sensing: Models and methods for image processing, 2nd ed. New York: Academic Press. Google Scholar
  24. Swayze, G.A. 1997. The hydrothermal and structural history of the Cuprite Mining District, southwestern Nevada: An integrated geological and geophysical approach. PhD dissertation, University of Colorado Boulder.Google Scholar
  25. Thai, B., and G. Healey. 2002. Invariant subpixel material detection in hyperspectral imagery. IEEE Transactions on Geoscience and Remote Sensing 40(3): 599–608, March 2002.Google Scholar
  26. Tzou, K.H. 1987. Progressive image transmission: A review and comparison of techniques. Optical Engineering 26(7): 581–589.CrossRefGoogle Scholar
  27. Winter, M.E. 1999a. Fast autonomous spectral endmember determination in hyperspectral data. In Proceedings of 13th international conference on applied geologic remote sensing, Vancouver, B.C., Canada, vol. II, 337–344, 1999.Google Scholar
  28. Winter, M.E. 1999b. N-finder: An algorithm for fast autonomous spectral endmember determination in hyperspectral data. In Proceedings of Image spectrometry V, Proc. SPIE 3753, 266–277, 1999.Google Scholar
  29. Wu, C.-C., C.S. Lo, and C.-I Chang. 2009. Improved process for use of a simplex growing algorithm for endmember extraction. IEEE Geoscience and Remote Sensing Letters 6(3): 523–527, July 2009.Google Scholar
  30. Xiong, W., C.-C. Wu, C.-I Chang, K. Kapalkis, and H.M. Chen. 2011. Fast algorithms to implement N-FINDR for hyperspectral endmember extraction. IEEE Journal of Selected Topics in Applied Earth Observation and Remote Sensing 4(3): 545–564, September, 2011.Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2016

Authors and Affiliations

  1. 1.BaltimoreUSA

Personalised recommendations