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Hyperspectral Target Detection Based on Kernels

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Optical Imaging Sensors and Systems for Homeland Security Applications

Part of the book series: Advanced Sciences and Technologies for Security Applications ((ASTSA,volume 2))

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Summary

In this chapter, linear signal or target detection algorithms are extended to nonlinear versions by using kernel-based methods. In kernel-based methods, learning is implicitly performed in a high-dimensional feature space where high order correlation or nonlinearity within the data are exploited. Nonlinear realization is mainly pursued to reduce data complexity in a high-dimensional feature space and consequently provide simpler decision rules for data discrimination.

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References

  1. Manolakis D and Shaw G. (2000). “Detection algorithms for hyperspectral imaging applications.” IEEE Signal Process. Mag., 19(1):29–43.

    Article  ADS  Google Scholar 

  2. Harsanyi JC and Chang C-I. (1994). “Hyperspectral image classification and dimensionality reduction: An orthogonal subspace projection approach.” IEEE Trans. Geosci. Remote Sensing, 32(4):779–785.

    Article  ADS  Google Scholar 

  3. Reed IS and Yu X. (1990). “Adaptive multiple-band CFAR detection of an optical pattern with unknown spectral distribution.” IEEE Trans. Acoustics, Speech Signal Process., 38(10):1760–1770.

    Article  ADS  Google Scholar 

  4. Chang C-I, Zhao X-L, Althouse MLG, and Pan JJ. (1998). “Least squares subspace projection approach to mixed pixel classification for hyperspectral images.” IEEE Trans. Geosci. Remote Sensing, 36(3):898–912.

    Article  ADS  Google Scholar 

  5. Healey G and Slater D. (1999). “Models and methods for automated material identification in hyperspectral imagery acquired under unknown illumination and atmospheric conditions.” IEEE Trans. Geosci. Remote Sensing, 37(6):2706–2717.

    Article  ADS  Google Scholar 

  6. Scharf LL and Friedlander B. (1994). “Matched subspace detectors.” IEEE Trans. Signal Process., 42(8):2146–2157.

    Article  ADS  Google Scholar 

  7. Schweizer SM and Moura JMF. (2001). “Efficient detection in hyperspectral imagery.” IEEE Trans. Image Process., 10:584–597.

    Article  MATH  ADS  Google Scholar 

  8. Stein DWJ, Beaven SG, Hoff LE, Winter EM, Schaum AP, and Stocker AD. (2002). “Anomaly detection from hyperspectral imagery.” IEEE Signal Process. Mag., 19:58–69.

    Article  ADS  Google Scholar 

  9. Kwon H, Der SZ, and Nasrabadi NM. (2001). “An adaptive unsupervised segmentation algorithm based on iterative spectral dissimilarity measure for hyperspectral imagery.” in: Proc. SPIE, 4310:144–152.

    Article  ADS  Google Scholar 

  10. Stein DWJ. (2001). “Stochastic compositional models applied to subpixel analysis of hyperspectral imagery.” in: Proc. SPIE, 4480:49–56.

    Article  ADS  Google Scholar 

  11. Kwon H. Der SZ, and Nasrabadi NM. (2003). “Adaptive anomaly detection using subspace separation for hyperspectral images.” Opt. Eng. 42(11):3342–3351.

    Article  ADS  Google Scholar 

  12. Chang C-I and Chiang S-S. (2002). “Anomaly detection and classification for hyperspectral imagery.” IEEE Trans. Geosci. Remote Sensing, 40(6):1314–1325.

    Article  ADS  Google Scholar 

  13. Yu X and Reed IS. (1993). “Comparative performance analysis of adaptive multispectral detectors.” IEEE Trans. Signal Process., 41(8):2639–2656.

    Article  ADS  MATH  Google Scholar 

  14. Manolakis D, Shaw G, and Keshava N. (2000). “Comparative analysis of hyperspectral adaptive matched filter detector.” in: Proc. SPIE, 4049:2–17.

    Article  ADS  Google Scholar 

  15. Robey FC, Fuhrmann DR and Kelly EJ. (1992). “A CFAR adaptive matched filter detector.” IEEE Trans. Aerospace and Elect. Syst., 28(1):208–216.

    Article  ADS  Google Scholar 

  16. Kraut S, Scharf LL, and McWhorter T. (2001). “Adaptive subspace detectors.” IEEE Trans. Signal Process., 49(1):208–216.

    Article  Google Scholar 

  17. Thai B and Healey G. (2002). “Invariant subpixel material detection in hyperspectral imagery.” IEEE Trans. Geosci. Remote Sensing, 40(3):599–608.

    Article  ADS  Google Scholar 

  18. Vapnik VN. (1999). The nature of statistical learning theory. Springer.

    Google Scholar 

  19. Schölkopf B and Smola AJ. (2002). Learning with Kernels. MIT.

    Google Scholar 

  20. Müller KR, Mika S, Rätsch G, Tsuda K, and Schölkopf B. (2001). “An introduction to kernel-based learning algorithms.” IEEE Trans. Neural Networks, (2):181–202.

    Article  Google Scholar 

  21. Lu J, Plataniotis KN, and Venetsanopoulos AN. (2003). “Face recognition using kernel direct discriminant analysis algorithm.” IEEE Trans. Neural Networks, 14(1):117–126.

    Article  Google Scholar 

  22. Paclik P, Pekalska E, and Duin RPW. (2001). “A generalized kernel approach to dissimilarity-based classification.” J. Mach. Learning, 2:175–211.

    MathSciNet  Google Scholar 

  23. Ruiz A and Lopez-de Teruel E. (2001). “Nonlinear kernel-based statistical patten analysis.” IEEE Trans. Neural Networks, 12:16–32.

    Article  Google Scholar 

  24. Schölkopf B, Smola AJ, and Müller K-R. (1999). “Kernel principal component analysis.” Neural Comput. (10):1299–1319.

    Article  Google Scholar 

  25. Baudat G and Anouar F. (2000). “Generalized discriminant analysis using a kernel approach.” Neural Comput. (12):2385–2404.

    Article  Google Scholar 

  26. Girolami M. (2002). “Mercer kernel-based clustering in feature space.” IEEE Trans. Neural Networks, 13(3):780–784.

    Article  Google Scholar 

  27. Joliffe IT. (1986). Principal Component Analysis. Springer-Verlag.

    Google Scholar 

  28. Scharf LL. (1991). Statistical Signal Processing. Addison-Wesley.

    Google Scholar 

  29. Chang C-I and Heinz DC. (2000). “Constrained subpixel target detection for remotely sensed imagery.” IEEE Trans. Geosci. Remote Sensing, 38(3):1144–1159.

    Article  ADS  Google Scholar 

  30. Settle JJ. (1996). “On the relationship between spectral unmixing and subspace projection.” IEEE Trans. Geosci. Remote Sensing, 34(4):1045–1046.

    Article  ADS  Google Scholar 

  31. Harsanyi JC. (1993). “Detection and Classification of Subpixel Spectral Signatures in Hyperspectral Image Sequences.” Ph.D. dissertation, Dept. Elect. Eng., Univ. of Maryland, Baltimore County.

    Google Scholar 

  32. Kraut S and Scharf LL. (1999). “The CFAR adaptive subspace detector is a scale invariant-invariant GLRT.” IEEE Trans. Signal Process., 47(9):2538–2541.

    Article  ADS  Google Scholar 

  33. Van Trees HL. (1968). Detection, Estimation, and Modulation Theory. Wiley.

    Google Scholar 

  34. Hastie T, Tibshirani R, and Friedman J. (2001). The Elements of Statistical Learning. Springer.

    Google Scholar 

  35. Strang G. (1986). Linear algebra and its applications. Harcourt Brace.

    Google Scholar 

  36. Jain AK, Murty MN, and Flynn PJ. (1999). “Data clustering: A review.” ACM Comput. Surveys, 31(3):264–323.

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. U.S. Army Research Laboratoryz, 2800 Powder Mill Road, Adelphi, MD, 20783

    Heesung Kwon & Nasser M. Nasrabadi

Authors
  1. Heesung Kwon
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  2. Nasser M. Nasrabadi
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Editor information

Editors and Affiliations

  1. Electrical & Computer Engineering Dept., University of Connecticut, 371 Fairfield Road, Unit 1157, Storrs, CT, 06269-1157, USA

    Bahram Javidi (Distinguished Professor of Electrical and Computer Engineering) (Distinguished Professor of Electrical and Computer Engineering)

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© 2006 Springer Science+Business Media, Inc.

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Kwon, H., Nasrabadi, N.M. (2006). Hyperspectral Target Detection Based on Kernels. In: Javidi, B. (eds) Optical Imaging Sensors and Systems for Homeland Security Applications. Advanced Sciences and Technologies for Security Applications, vol 2. Springer, New York, NY. https://doi.org/10.1007/0-387-28001-4_15

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