Kernel RX-algorithm: A nonlinear anomaly detector for hyperspectral imagery

IEEE Transactions on Geoscience and Remote Sensing

Volumn 43, Issue 2, 2005, Pages 388-397

  Kwon, Heesung ^a   Nasrabadi, Nasser M ^a  

^a U S ARMY RESEARCH LABORATORY   (United States)

Author keywords

Anomaly detection; Hyperspectral images; Kernel based learning; Kernels; Target detection

Indexed keywords

ALGORITHMS; BAND STRUCTURE; CLUTTER (INFORMATION THEORY); DATA ACQUISITION; ERROR DETECTION; IMAGE SENSORS; INFRARED RADIATION; NONLINEAR CONTROL SYSTEMS; PATTERN RECOGNITION; SIGNAL NOISE MEASUREMENT; SPECTROMETERS;

ANOMALY DETECTION; HYPERSPECTRAL IMAGES; KERNEL BASED LEARNING; KERNELS; TARGET DETECTION;

IMAGE ANALYSIS;

EID: 13144293109     PISSN: 01962892     EISSN: None     Source Type: Journal    
DOI: 10.1109/TGRS.2004.841487     Document Type: Article

References (29)

1. D. Manolakis and G. Shaw, "Detection algorithms for hyperspectral imaging applications," IEEE Signal Process, Mag., vol. 19, no. 1, pp. 29-43, Jan. 2002.
2. J. C. Harsanyi and C.-I. Chang, "Hyperspectral image classification and dimensionality reduction: An orthogonal subspace projection approach," IEEE Trans. Geosci. Remote Sens., vol. 32, no. 4, pp. 779-785, Jul. 1994.
3. 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., vol. 38, no. 10, pp. 1760-1770, Oct. 1990.
4. C.-I. Chang, X.-L. Zhao, M. L. G. Althouse, and J. J. Pan, "Least squares subspace projection approach to mixed pixel classification for hyperspectral images." IEEE Trans. Geosci. Remote Sens., vol. 36, no. 3, pp. 898-912, May 1998.
5. G. Healey and D. Slater, "Models and methods for automated material identification in hyperspectral imagery acquired under unknown illumination and atmospheric conditions," IEEE Trans. Geosci. Remote Sens., vol. 37, no. 6, pp. 2706-2717, Nov. 1999.
6. L. L. Scharf and B. Friedlander, "Matched subspace detectors," IEEE Trans. Signal Process., vol. 42, no. 8, pp. 2146-2157, Aug. 1994.
7. S. M. Schweizer and J. M. F. Moura, "Efficient detection in hyperspectral imagery," IEEE Trans. Image Process., vol. 10, no. 4, pp. 584-597, Apr. 2001.
8. 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 Process. Mag., vol. 19, no. 1, pp. 58-69, Jan. 2002.
9. H. Kwon, S. Z. Der, and N. M. Nasrabadi, "An adaptive unsupervised segmentation algorithm based on iterative spectral dissimilarity measure for hyperspectral imagery," Proc. SPIE, vol. 4310, pp. 144-152, Jan. 2001.
10. D. W. J. Stein, "Stochastic compositional models applied to subpixel analysis of hyperspectral imagery." Proc. SPIE, vol. 4480, pp. 49-56, Jul. 2001.
11. H. Kwon, S. Z. Der, and N. M. Nasrabadi, "Adaptive anomaly detection using subspace separation for hyperspectral images," Opt. Eng., vol. 42, no. 11, pp. 3342-3351, Nov. 2003.
12. C.-I. Chang and S.-S. Chiang, "Anomaly detection and classification for hyperspectral imagery," IEEE Trans. Geosci. Remote Sens., vol. 40, no. 6, pp. 1314-1325, Jun. 2002.
13. X. Yu and I. S. Reed, "Comparative performance analysis of adaptive multispectral detectors," IEEE Trans. Signal Process., vol. 41, no. 8, pp. 2639-2656, Aug. 1993.
14. C. M. Stellman, G. G. Hazel, F. Buchholtz, and J. V. Michalowicz, "Real-time hyperspectral detection and cuing," Opt. Eng., vol. 39, no. 7, pp. 1928-1935, Jul. 2000.
15. B. Schökopf and A. J. Smola, Learning with Kernels. Cambridge, MA: The MIT Press. 2002.
16. J. Lu, K. N. Plataniotis, and A. N. Venetsanopoulos, "Face recognition using kernel direct discriminant analysis algorithm," IEEE Trans. Neural Networks, vol. 14, no. 1, pp. 117-126, Jan. 2003.
17. P. P. Pekalska and E. R. P. W. Duin, "A generalized kernel approach to dissimilarity-based classification,'' J. Mach. Learn., vol. 2, pp. 175-211, 2001.
18. A. Ruiz and E. Lopez-de Teruel, "Nonlinear kernel-based statistical pattern analysis." IEEE Trans. Neural Networks, vol. 12, no. 1, pp. 16-32, Jan. 2001.
19. B. Schökopf, A. J. Smola, and K.-R. Müller, "Kernel principal component analysis," Neural Comput., no. 10, pp. 1299-1319, 1999.
20. G. Baudat and F. Anouar, "Generalized discriminant analysis using a kernel approach," Neural Comput., no. 12, pp. 2385-2404, 2000.
21. M. Girolami, "Mercer kernel-based clustering in feature space," IEEE Trans. Neural Networks, vol. 13, no. 3, pp. 780-784, May 2002.
22. H. Kwon and N. M. Nasrabadi, "Hyperspectral target detection using kernel matched subspace detector," in Proc. IEEE Int. Conf. Image Processing, Singapore, Oct. 2004, pp. 3327-3330.
23. I. T. Joliffe, Principal Component Analysis. Berlin, Germany: Springer-Verlag, 1986.
24. R. C. Williamson, A. J. Smola, and B Schölkopf, "Generalization performance of regularization networks and support vector machines via entropy numbers of compact operators," IEEE Trans. Inf. Theory, vol. 47, no. 6, pp. 2516-2532, Sep. 2001.
25. F. R. Bach and M. I. Jordan, "Kernel independent component analysis," J. Mach. Learn. Res., vol. 47, pp. 1-48, 2002.
26. D. Cremers, T. Kohlberger, and B. Schölkopf, "Shape statistics in kernel space for variational image segmentation," Pattern Recognit., vol. 36, pp. 1929-1943, 2003.
27. F. R. Bach and M. I. Jordan, "Learning graphical models with mercer kernels," Adv. Neural Inf. Process. Syst., vol. 15, 2003.
28. A. K. Jain, M. N. Murty, and P. J. Flynn, "Data clustering: A review," ACM Comput. Surv., vol. 31, no. 3, pp. 264-323, 1999.
29. G. Strang, Linear Algebra and Its Applications. Orlando, FL: Harcourt Brace, 1986.