A non-parametric approach to anomaly detection in hyperspectral images

Proceedings of SPIE - The International Society for Optical Engineering

Volumn 7830, Issue , 2010, Pages

  Veracini, Tiziana ^a   Matteoli, Stefania ^a   Diani, Marco ^a   Corsini, Giovanni ^a   De Ceglie, Sergio U ^b  

^a UNIVERSITY OF PISA   (Italy)

^b Centro Interforze Studi Applicazioni Militari (CISAM)   (Italy)

Author keywords

anomaly detection; Bayesian approach; density estimation; hyperspectral imagery; kernel bandwidth estimation; Parzen windows

Indexed keywords

ANOMALY DETECTION; BAYESIAN APPROACHES; DENSITY ESTIMATION; HYPERSPECTRAL IMAGERY; KERNEL BANDWIDTH ESTIMATION; PARZEN WINDOWS;

BANDWIDTH; BAYESIAN NETWORKS; ESTIMATION; PROBABILITY DISTRIBUTIONS; REMOTE SENSING; SIGNAL DETECTION; SIGNAL PROCESSING; SPECTRUM ANALYSIS; SPECTRUM ANALYZERS;

PROBABILITY DENSITY FUNCTION;

EID: 78649746398     PISSN: 0277786X     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1117/12.865073     Document Type: Conference Paper

References (16)

1. Kay, S. M., Fundamentals of Statistical Processing: Detection Theory, Prentice Hall, Ed. US (1998).
2. Hytla, P. C., Hardie, R. C., Eismann, M. T., and Meola, J., "Anomaly detection in hyperspectral imagery: comparison of methods using diurnal and seasonal data," J. Appl. Remote Sens. 3(1), 033546-033546-30 (2009).
3. Manolakis, D. and Shaw, G., "Detection algorithms for hyperspectral imaging applications," IEEE Signal Process. Mag. 19(1), 29-43 (2002).
4. Reed, I. S., and Yu, X., "Adaptive multiple-band CFAR detection of an optical pattern with unknown spectral distribution," IEEE Trans. Acoust., Speech, Signal Process. 38(10), 1760-1770 (1990).
5. Stein, D.W. J., Beaven, S. G., Hoff, L. E., Winter, E. M., Schaum, A. P., and Stocker, A. D., "Anomaly detection from hyperspectral imagery," IEEE Signal Process. Mag. 19(1), 58-69 (2002).
6. Yu, X. and Reed, I. S., "Comparative performance analysis of adaptive multispectral detectors," IEEE Trans. Signal Process. 41(8), 2639-2656 (1993).
7. Marden, D. and Manolakis, D., "Modeling hyperspectral imaging data," in Proc. of SPIE 5093, 253-262 (2003).
8. Härdle, W., Müller, M., Sperlich, S. and Werwatz, A., Nonparametric and Semiparametric Models, Springer-Verlag, Berlin (2004).
9. Duda, R. O., Hart, P. E. and Stork, D. G., Pattern Classification (2nd Ed.), John Wiley & Sons, New York (2000).
10. Bishop, C. M., "Pattern Recognition and machine learning," Springer, Singapore (2006).
11. Sain, S.R., Baggerly, K.A. and Scott, D.W., "Cross-Validation of Multivariate Densities," Journal of the American Statistical Association 89(427), 807-817 (1994).
12. Wand, M.P. and Jones, M.C., "Multivariate Plug-in Bandwidth Selection," Computational Statistics 9, 97-116 (1994).
13. Scott, D.W., "Multivariate Density Estimation: Theory, Practice, and Visualization," John Wiley & Sons, New York (1992).
14. Bors, A.G., Nasios, N.; "Kernel Bandwidth Estimation for Nonparametric Modeling", IEEE Trans. Syst., Man, Cybern. B, Cybern. 39(6), 1543-1555 (2009).
15. Acito, N., M., Diani and G., Corsini, "A new algorithm for robust estimation of the signal subspace in hyperspectral images in presence of rare signal components," IEEE Trans. Geosci. Remote Sens. 47(11), 3844-3856 (2009).
16. Fawcett, T., "An introduction to ROC analysis," Pattern Recognit. Lett. 27, Elsevier, 861-874 (2005).