Detecting changes in hyperspectral imagery using a model-based approach

IEEE Transactions on Geoscience and Remote Sensing

Volumn 49, Issue 7, 2011, Pages 2647-2661

  Meola, Joseph ^a   Eismann, Michael T ^a   Moses, Randolph L ^b   Ash, Joshua N ^b  

^a AIR FORCE RESEARCH LABORATORY   (United States)

^b Ohio State University   (United States)

Author keywords

Change detection; hyperspectral; hypothesis testing; image analysis; optimization; physical model

Indexed keywords

AREA OF INTEREST; CHANGE DETECTION; DATA SETS; FALSE ALARMS; GENERALIZED LIKELIHOOD-RATIO TESTS; HYPERSPECTRAL; HYPERSPECTRAL IMAGERY; HYPOTHESIS TESTING; MAXIMUM LIKELIHOOD ESTIMATE; MISREGISTRATION; MODEL BASED APPROACH; PARALLAX ERROR; PHYSICAL MODEL; SIMULATION RESULT; STATISTICAL HYPOTHESIS TEST;

ERROR DETECTION; GEOMETRICAL OPTICS; MAXIMUM LIKELIHOOD ESTIMATION; REMOTE SENSING; SIGNAL DETECTION; STATISTICAL TESTS;

PIXELS;

ATMOSPHERIC CORRECTION; DATA SET; DETECTION METHOD; ERROR ANALYSIS; HYPOTHESIS TESTING; IMAGE ANALYSIS; MAXIMUM LIKELIHOOD ANALYSIS; OPTIMIZATION; PARAMETERIZATION; PIXEL;

EID: 79959705715     PISSN: 01962892     EISSN: None     Source Type: Journal    
DOI: 10.1109/TGRS.2011.2109726     Document Type: Article

References (54)

1. P. Coppin and M. Bauer, "Digital change detection in forest ecosystems," Remote Sens. Rev., vol. 13, pp. 207-234, 1996.
2. J. B. Collins and C. E.Woodcock, "An assessment of several linear change detection techniques for mapping forest mortality using multi-temporal Landsat TM data," Remote Sens. Environ., vol. 56, no. 1, pp. 66-77, Apr. 1996.
3. R. N. Fraser, "Hyperspectral remote sensing of turbidity and chlorophyll a among Nebraska Sand Hills Lakes," Int. J. Remote Sens., vol. 19, no. 8, pp. 1579-1589, 1998. (Pubitemid 28560215)
4. G. Nagy, T. Zhang, W. R. Franklin, E. Landis, E. Nagy, and D. T. Keane, "Volume and surface area distributions of cracks in concrete," in Proc. Visual Form, 2001, pp. 759-768. (Pubitemid 33276078)
5. A. Huertas and R. Nevatia, "Detecting changes in aerial views of manmade structures," Image Vis. Comput., vol. 18, no. 8, pp. 583-596, May 2000. (Pubitemid 30589186)
6. S.Watanabe, K. Miyajima, and N. Mukawa, "Detecting changes of buildings from aerial images using shadow and shading model," in Proc. ICPR, 1998, pp. 1408-1412.
7. R. Collins, A. Lipton, and T. Kanade, "Introduction to the special section on video surveillance," IEEE Trans. Pattern Anal. Mach. Intell., vol. 22, no. 8, pp. 745-746, Aug. 2000.
8. R. Streit, M. Graham, and M.Walsh, "Tracking in hyper-spectral data," in Proc. 5th Int. Conf. Inf. Fusion, 2002, vol. 2, pp. 852-859.
9. J. Blackburn, M. Mendenhall, A. Rice, P. Shelnutt, N. Soliman, and J. Vasquez, "Feature aided tracking with hyperspectral imagery," in Proc. SPIE, 2007, vol. 6699, pp. 66990S-1-66990S-12.
10. D. Rey, G. Subsol, H. Delingette, and N. Ayache, "Automatic detection and segmentation of evolving processes in 3D medical images: Application to multiple sclerosis," Med. Image Anal., vol. 6, no. 2, pp. 163-179, Jun. 2002. (Pubitemid 35282824)
11. A. Schaum, E. Allman, J. Kershenstein, and D. Alexa, "Hyperspectral change detection in high clutter using elliptically contoured distributions," Proc. SPIE, vol. 6565, pp. 656515-1-656515-12, 2007.
12. A. Schaum and M. McHugh, "Analytic methods of image registration: Displacement estimation and resampling," Naval Res. Lab., Washington, DC, Tech. Rep. 9298, 1991.
13. J. R. Schott, Remote Sensing: The Image Chain Approach, 2nd ed. New York: Oxford Univ. Press, 2007.
14. R. J. Radke, S. Andra, O. Al-Kofahi, and B. Roysam, "Image change detection algorithms: A systematic survey," IEEE Trans. Image Process., vol. 14, no. 3, pp. 294-307, Mar. 2005. (Pubitemid 40351907)
15. A. Singh, "Digital change detection techniques using remotely-sensed data," Int. J. Remote Sens., vol. 10, no. 6, pp. 989-1003, Jun. 1989.
16. A. Schaum and A. Stocker, "Long-interval chronochrome target detection," in Proc. Int. Symp. Spec. Sens. Res., 1997, pp. 1-14.
17. A. Schaum and A. Stocker, "Linear chromodynamics models for hyperspectral target detection," in Proc. IEEE Aerosp. Conf., Feb. 2003, pp. 77-90.
18. A. Schaum and A. Stocker, "Hyperspectral change detection and supervised matched filtering based on covariance equalization," Proc. SPIE, vol. 5425, pp. 77-90, 2004.
19. R. Mayer, F. Bucholtz, and D. Scribner, "Object detection by using 'whitening/dewhitening' to transform target signatures in multitemporal hyperspectral and multispectral imagery," IEEE Trans. Geosci. Remote Sens., vol. 41, no. 5, pp. 1136-1142, May 2003.
20. J. Meola, "Analysis of hyperspectral change detection as affected by vegetation and illumination variation,"M.S. thesis, Univ. Dayton, Dayton, OH, 2006.
21. J. Meola, M. T. Eismann, K. J. Barnard, and R. C. Hardie, "Analysis of hyperspectral change detection as affected by vegetation and illumination variation," Proc. SPIE, vol. 6565, pp. 65651S-1-65651S-12, 2007.
22. M. T. Eismann and J. Meola, "Use of spectral clustering to enhance clutter suppression for hyperspectral change detection," Proc. SPIE, vol. 6565, pp. 65651T-1-65651T-12, 2007.
23. M. T. Eismann, J. Meola, A. Stocker, S. Beaven, and A. Schaum, "Airborne hyperspectral detection of small targets," Appl. Opt., vol. 47, no. 28, pp. F27-F45, Oct. 2008.
24. M. T. Eismann, J. Meola, A. Stocker, S. Beaven, and A. Schaum, "Hyperspectral change detection in the presence of diurnal and seasonal variations," IEEE Trans. Geosci. Remote Sens., vol. 46, no. 1, pp. 237-249, Jan. 2008.
25. C. Clifton, "Change detection in overhead imagery using neural networks," Appl. Intell., vol. 18, no. 2, pp. 215-234, Mar./Apr. 2003.
26. J. Theiler and S. Perkins, "Resampling approach for anomalous change detection," Proc. SPIE, vol. 6565, pp. 65651U-1-65651U-12, 2007.
27. M. J. Carlotto, "Non-linear mean-square estimation with applications in remote sensing," Proc. SPIE, vol. 2758, pp. 206-217, 1996.
28. M. J. Carlotto, "Nonlinear background estimation and change detection for wide area search," Opt. Eng., vol. 39, no. 5, pp. 1223-1229, May 2000.
29. J. Theiler, "Quantitative comparison of quadratic covariance-based anomalous change detectors," Appl. Opt., vol. 47, no. 28, pp. F12-F26, Oct. 2008.
30. A. Nielsen, "The regularized iteratively reweighted MAD method for change detection in multi-and hyperspectral data," IEEE Trans. Image Process., vol. 16, no. 2, pp. 463-478, Feb. 2007. (Pubitemid 46421687)
31. 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.
32. 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.
33. T. Kasetkasem and P. K. Varshney, "An image change detection algorithm based on Markov random field models," IEEE Trans. Geosci. Remote Sens., vol. 40, no. 8, pp. 1815-1823, Aug. 2002. (Pubitemid 35170401)
34. L. Bruzzone and D. F. Prieto, "Automatic analysis of the difference image for unsupervised change detection," IEEE Trans. Geosci. Remote Sens., vol. 38, no. 3, pp. 1171-1182, May 2000.
35. K. Chen, C. Huo, J. Cheng, Z. Zhou, and H. Lu, "Change detection based on adaptive Markov random fields," in Proc. 19th Int. Conf. Pattern Recog., Dec. 2008, pp. 1-4.
36. J. Meola, M. T. Eismann, R. L. Moses, and J. N. Ash, "A model-based approach to hyperspectral change detection," Proc. SPIE, vol. 7695, pp. 76951G-1-76951G-12, 2010.
37. K. Chandra and G. Healey, "Recovering surface properties for hyperspectral scenes," Proc. SPIE, vol. 6233, pp. 62331H-1-62331H-9, 2006.
38. K. Chandra and G. Healey, "Using coupled subspace methods for reflectance/illumination separation," IEEE Trans. Geosci. Remote Sens., vol. 46, no. 1, pp. 284-290, Jan. 2008. (Pubitemid 350281159)
39. A. Berk, L. Bernstein, and D. Robertson, "MODTRAN: A moderate resolution model for LOWTRAN 7," Geophys. Lab., Bedford, MA, Tech. Rep. GL-TR-89-0122, 1989.
40. A. Berk, G. P. Anderson, L. S. Bernstein, P. K. Acharya, H. Dothe, M. W. Matthew, S. M. Adler-Golden, J. H. Chetwynd, S. C. Richtsmeier, B. Pukall, C. L. Allred, L. S. Jeong, and M. L. Hoke, "MODTRAN4: Radiative transfer modeling for atmospheric correction," Proc. SPIE, vol. 3756, pp. 348-353, 1999. (Pubitemid 30513948)
41. S. M. Kay, Fundamentals of Statistical Signal Processing: Detection Theory. Upper Saddle River, NJ: Prentice-Hall, 1998.
42. G. Healey and R. Kondepudy, "Radiometric CCD camera calibration and noise estimation," IEEE Trans. Pattern Anal. Mach. Intell., vol. 16, no. 3, pp. 267-276, Mar. 1994.
43. G. Holst, CCD Arrays Cameras and Displays, 2nd ed. Winter Park, FL: JCD Publishing, 1998.
44. J. P. Kerekes and J. E. Baum, "Hyperspectral imaging and system modeling," Lincoln Lab. J., vol. 14, no. 1, pp. 117-130, 2003.
45. P. Suen, G. Healey, and D. Slater, "The impact of viewing geometry on material discriminability in hyperspectral images," IEEE Trans. Geosci. Remote Sens., vol. 39, no. 7, pp. 1352-1360, Jul. 2001.
46. D. P. Bertsekas, Nonlinear Programming, 2nd ed. Belmont,MA: Athena Scientific, 1999.
47. J. C. Bezdek and R. J. Hathaway, "Some notes on alternating optimization," in Proc. AFSS Int. Conf. Fuzzy Syst., Calcutta, India, 2002, pp. 288-300.
48. P. E. Gill, W. Murray, and M. H. Wright, Practical Optimization. London, U.K.: Academic, 1981.
49. T. F. Coleman and Y. Li, "A reflective Newton method for minimizing a quadratic function subject to bounds on some of the variables," SIAM J. Optim., vol. 6, no. 4, pp. 1040-1058, 1996.
50. E. Karpouzli and T. Malthus, "The empirical line method for the atmospheric correction of IKONOS imagery," Int. J. Remote Sens., vol. 24, no. 5, pp. 1143-1150, 2003. (Pubitemid 36421229)
51. L. S. Bernstein, S. M. Adler-Golden, R. L. Sundberg, R. Y. Levine, T. C. Perkins, A. Berk, A. J. Ratkowski, G. Felde, andM. L. Hoke, "A new method for atmospheric correction and aerosol optical property retrieval for VIS-SWIR multi-and hyperspectral imaging sensors: QUAC (QUick Atmospheric Correction)," in Proc. IEEE Geosci. Remote Sens. Symp., 2005, vol. 5, pp. 3549-3552.
52. T. Perkins, S. Adler-Golden, M. Matthew, A. Berk, G. Anderson, J. Gardner, and G. Felde, "Retrieval of atmospheric properties from hyperand multi-spectral imagery with FLAASH atmospheric correction algorithm," Proc. SPIE, vol. 5979, pp. 59790E-1-59790E-11, 2005.
53. L. L. Scharf and D. W. Tufts, "Rank reduction for modeling stationary signals," IEEE Trans. Acoust., Speech, Signal Process., vol. ASSP-35, no. 3, pp. 350-355, Mar. 1987.
54. National Aeronautic and Space Administration: Jet Propulsion Lab, ASTER Spectral Library, Jan. 2010. [Online]. Available: http://speclib.jpl. nasa.gov/