A comparative study of linear and nonlinear anomaly detectors for hyperspectral imagery

Proceedings of SPIE - The International Society for Optical Engineering

Volumn 6565, Issue , 2007, Pages

  Goldberg, Hirsh ^a   Nasrabadi, Nasser M ^b  

^a UNIVERSITY OF MARYLAND   (United States)

^b U S ARMY RESEARCH LABORATORY   (United States)

Author keywords

Anomaly detection; Eigenspace Separation Transform (EST); Hyperspectral imagery; Kernel fisher discriminant; Kernel PCA; Kernel based machine learning; Kernels

Indexed keywords

ANOMALY DETECTION; EIGENSPACE SEPARATION TRANSFORM (EST); HYPERSPECTRAL IMAGERY; KERNEL BASED MACHINE LEARNING; KERNEL FISHER DISCRIMINANT; KERNELS;

EIGENVALUES AND EIGENFUNCTIONS; FAULT DETECTION; IMAGE ANALYSIS; LEARNING SYSTEMS; PIXELS; PRINCIPAL COMPONENT ANALYSIS;

IMAGING SYSTEMS;

EID: 35948987265     PISSN: 0277786X     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1117/12.719932     Document Type: Conference Paper

References (16)

1. H. Kwon, N. Nasrabadi, and S. Der, "Adaptive anomaly detection using subspace separation for hyperspectral imagery," Optical Engineering 42, pp. 3342-3351, Nov. 2003.
2. D. Manolakis and G. Shaw, "Detection algorithms for hyperspectral imaging applications," IEEE Signal Processing Magazing 19, pp. 29-43, Jan. 2002.
3. D. W. J. Stein, S. G. Beaven, L. E. Hoff, E. M. Winter, A. P. Schaum, and A. D. Stocker, "Anomaly detection from hyperspectral imagery," IEEE Signal Processing Magazine 19, pp. 58-69, Jan. 2002.
4. D. Manolakis, D. Marden, and G. A. Shaw, "Hyperspectral image processing for automatic target detection applications," Lincoln Laboratory Journal 14(1), pp. 79-116, 2003.
5. I. S. Reed and X. Yu, "Adaptive multiple-band cfar detection of an optical pattern with unknown spectral distribution," IEEE Trans. Acoust. Speech Signal Process. 38, pp. 1760-1770, Oct. 1990.
6. H. Kwon and N. M. Nasrabadi, "Kernel RX-algorithm: A nonlinear anomaly detector for hyperspectral imagery," IEEE Trans. on Geoscience and Remote Sensing 43, pp. 388-397, Feb. 2005.
7. A. Banerjee, P. Burlina, and C. Diehl, "A support vector method for anomaly detectino in hyperspectral imagery," IEEE Trans, on Geoscience and Remote Sensing 44, pp. 2282-2291, Aug. 2006.
8. K. I. Ranney and M. Soumekh, "Hyperspectral anomaly detection within the signal subspace," IEEE Geoscience and Remote Sensing Letters 3, pp. 312-316, July 2006.
9. I. T. Joliffe, Principal Component Analysis, Springer-Verlag, Berlin, Germany, 1986.
10. R. O. Duda, P. E. Hart, and D. G. Stork, Pattern Classification, John Wiley & Sons, Inc., New York, 2 ed., 2001.
11. D. Torrieri, "The eigenspace transform for neural network classifiers," Neural Networks 12, pp. 419-427, Apr. 1999.
12. B. Schölkopf, A. J. Smola, and K.-R. Müller, "Kernel principal component analysis," Neural Computation (10), pp. 1299-1319, 1999.
13. S. Mika, G. Ratsch, J. Weston, B. Schölkopf, and K.-R. Müller, "Constructing descriptive and discriminative non-linear feature: Rayleigh coefficients in kernel feature space," IEEE Trans. on Pattern Analysis and Machine Intell. 25(5), pp. 623-628.
14. L. A. Chan, N. M. Nasrabadi, and D. Torrieri, "Eigenspace transform for automatic clutter rejection," Optical Engineering, pp. 564-573, 2001.
15. B. Schokopf and A. J. Smola, Learning with Kernels, MIT Press, Cambridge, MA, 2002.
16. J. Shawe-Taylor and N. Cristianini, Kernel Methods for Pattern Analysis, Cambridge University Press, Cambridge, UK, 2004.