Anomaly detection based on wavelet domain GARCH random field modeling

IEEE Transactions on Geoscience and Remote Sensing

Volumn 45, Issue 5, 2007, Pages 1361-1373

  Noiboar, Amir ^b   Cohen, Israel ^a,b  

^a IEEE   (Israel)

^b TECHNION ISRAEL INSTITUTE OF TECHNOLOGY   (Israel)

Author keywords

Anomaly detection; Gaussian Markov random field (GMRF); Generalized Autoregressive Conditional Heteroscedasticity (GARCH); Image segmentation; Image texture analysis; Matched subspace detector; Multiscale representation; Object recognition

Indexed keywords

ANOMALY DETECTION; GAUSSIAN MARKOV RANDOM FIELD (GMRF); GENERALIZED AUTOREGRESSIVE CONDITIONAL HETEROSCEDASTICITY (GARCH); IMAGE TEXTURE ANALYSIS; MATCHED SUBSPACE DETECTOR; MULTISCALE REPRESENTATION;

IMAGE SEGMENTATION; ONE DIMENSIONAL; RANDOM PROCESSES; TIME SERIES ANALYSIS; WAVELET ANALYSIS;

OBJECT RECOGNITION;

EID: 34247549367     PISSN: 01962892     EISSN: None     Source Type: Journal    
DOI: 10.1109/TGRS.2007.893741     Document Type: Article

References (34)

1. 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.
2. E. A. Ashton, "Detection of subpixel anomalies in multispectral infrared imagery using an adaptive Bayesian classifier," IEEE Trans. Geosci. Remote Sens., vol. 36, no. 2, pp. 506-517, Mar. 1998.
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 Process. Mag., vol. 19, no. 1, pp. 58-69, Jan. 2002.
4. D. Manolakis and G. Shaw, "Detection algorithms for hyperspectral imaging applications," IEEE Signal Process. Mag., vol. 19, no. 1, pp. 29-43, Jan. 2002.
5. M. Mignotte, C. Collet, P. Perez, and P. Bouthemy, "Three-class Markovian segmentation of high-resolution sonar images," Comput. Vis. Image Process., vol. 76, no. 3, pp. 191-204, Nov. 1999.
6. S. Reed, Y. Petillot, and J. Bell, "An automatic approach to the detection and extraction of mine features in sidescan sonar," IEEE J. Ocean. Eng., vol. 28, no. 1, pp. 90-105, Jan. 2003.
7. E. Dura, Y. Zhang, X. Liao, G. Dobeck, and L. Carin, "Active learning for detection of mine-like objects in side-scan sonar imagery," IEEE J. Ocean. Eng., vol. 30, no. 2, pp. 360-371, Apr. 2005.
8. R. N. Strickland and H. I. Hahn, "Wavelet transform methods for object detection and recovery," IEEE Trans. Image Process., vol. 6, no. 5, pp. 724-735, May 1997.
9. I. Cohen and R. R. Coifman, "Local discontinuity measures for 3-D seismic data," Geophysics, vol. 67, no. 6, pp. 1933-1945, Nov./Dec. 2002.
10. A. Noiboar and I. Cohen, "Anomaly detection in three dimensional data based on Gauss Markov random field modeling," in Proc. 23rd IEEE Conv. Elect. and Electron. Eng., Tel-Aviv, Israel, Sep. 2004, pp. 448-451.
11. X. Yu, I. S. Reed, W. Karske, and A. D. Stocker, "A robust adaptive multi-spectral object detection by using wavelet transform," in Proc. IEEE ICASSP, 1992, pp. V-141-V-144.
12. A. F. Laine, S. Schuler, J. Fan, and W. Huda, "Mammographic feature enhancement by multiscale analysis," IEEE Trans. Med. Imag., vol. 13, no. 4, pp. 725-740, Dec. 1994.
13. J. W. Woods, "Two-dimensional discrete Markovian fields," IEEE Trans. Inf. Theory, vol. IT-18, no. 2, pp. 232-240, Mar. 1972.
14. S. M. Schweizer and J. M. F. Moura, "Hyperspectral imagery: Clutter adaptation in anomaly detection," IEEE Trans. Inf. Theory, vol. 46, no. 5, pp. 1855-1871, Aug. 2000.
15. 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.
16. G. G. Hazel, "Multivariate Gaussian MRF for multispectral scene segmentation and anomaly detection," IEEE Trans. Geosci. Remote Sens., vol. 38, no. 3, pp. 1199-1211, May 2000.
17. A. Goldman and I. Cohen, "Anomaly subspace detection based on a multi-scale Markov random field model," Signal Process., vol. 85, no. 3, pp. 463-479, Mar. 2005.
18. A. S. Willsky, "Multiresolution Markov models for signal and image processing," Proc. IEEE, vol. 90, no. 8, pp. 1396-1458, Aug. 2002.
19. K. Fukunaga, Introduction to Statistical Pattern Recognition, 2nd ed. New York: Academic, 1990.
20. L. L. Scharf and B. Friedlander, "Matched subspace detectors," IEEE Trans. Signal Process., vol. 42, no. 8, pp. 2146-2157, Aug. 1994.
21. S. Kraut, L. L. Scharf, and L. T. McWhorter, "Adaptive subspace detectors," IEEE Trans. Signal Process., vol. 49, no. 1, pp. 1-16, Jan. 2001.
22. R. W. Buccigrossi and E. P. Simoncelli, "Image compression via joint statistical characteristics in the wavelet domain," IEEE Trans. Image Process., vol. 8, no. 12, pp. 1688-1701, Dec. 1999.
23. S. G. Mallat, "A theory for multiresolution signal decomposition: The wavelet representation," IEEE Trans. Pattern Anal. Mach. Intell., vol. 11, no. 7, pp. 674-693, Jul. 1989.
24. A. Srivastava, "Stochastic models for capturing image variability," IEEE Signal Process. Mag., vol. 19, no. 5, pp. 63-76, Sep. 2002.
25. T. Bollerslev, "Generalized autoregressive conditional heteroscedasticity," J. Econom., vol. 31, no. 3, pp. 307-327, Apr. 1986.
26. R. F. Engle, "Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation," Econometrica, vol. 50, no. 4, pp. 987-1007, Jul. 1982.
27. J. D. Hamilton, Time Series Analysis. Princeton, NJ: Princeton Univ. Press, 1994.
28. A. Noiboar and I. Cohen, "Two-dimensional GARCH model with application to anomaly detection," presented at the 13th European Signal Processing Conf., Istanbul, Turkey, Sep. 2005, Paper 1594.
29. S. Reed, Y. Petillot, and J. Bell, "Automated approach to classification of mine-like objects in sidescan sonar using highlight and shadow information," Proc. Inst. Electr. Eng. - Radar, Sonar Navigation, vol. 151, no. 1, pp. 48-56, Feb. 2004.
30. E. K. Berndt, B. H. Hall, R. E. Hall, and J. A. Hausman, "Estimation and inference in nonlinear structural models," Ann. Econ. Soc. Meas., vol. 3/4, pp. 653-665, 1974.
31. Q. Wu, Z. Liu, T. Chen, Z. Xiong, and K. R. Castleman, "Subspace-based prototyping and classification of chromosome images," IEEE Trans. Image Process., vol. 14, no. 9, pp. 1277-1287, Sep. 2005.
32. P. N. Belhumeur, J. P. Hespanha, and D. J. Kriegman, "Eigenfaces vs. fisherfaces: Recognition using class specific linear projection," IEEE Trans. Pattern Anal. Mach. Intell., vol. 19, no. 7, pp. 711-720, Jul. 1997.
33. P. R. Hansen and A. Lunde, A Comparison of Volatility Models: Does Anything Beat a GARCH(1,1)? Aarhus, Denmark: Center Analytical Finance, Univ. Aarhus, Mar. 2001. working paper.
34. E. Dura, J. M. Bell, and D. M. Lane, "Superellipse fitting for the classification of mine-like shapes in side-scan sonar images," in Proc. Oceans MTS/IEEE, 2002, vol. 1, pp. 23-28.