Remotely sensed change detection based on artificial neural networks

Photogrammetric Engineering and Remote Sensing

Volumn 65, Issue 10, 1999, Pages 1187-1194

  Dai, X Long ^a   Khorram, Siamak ^a  

^a NORTH CAROLINA STATE UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 0032874004     PISSN: 00991112     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (36)

1. Anderson, J.R., E.E. Hardy, J.T. Roach, and R.E. Witmer, 1976. A Land Use and Land Cover Classification System for Use with Remote Sensing Data, USGS Prof. Paper 964, U.S. Geological Survey, U.S. Government Printing Office, Washington, D.C., 28 p.
2. Asrar, G., and R. Greenstone (editors), 1995. 1995 MTPE/EOS Reference Handbook, NASA/Goddard Space Flight Center, Greenbelt, Maryland, 277 p.
3. Baronti, S., R. Carla, S. Sigimondi, and L. Alparone, 1994. Principal component analysis for change detection on polarimetric multitemporal SAR data, Proc. IEEE/IGARSS '94, Pasadena, California, pp. 2152-2154.
4. Benediktsson, J.A., P.H. Swain, and O.K. Ersoy, 1990. Neural network approaches versus statistical methods in classification of multisource remote sensing data, IEEE Trans. on Geoscience and Remote Sensing, 28:540-551.
5. Bischof, H., W. Schneider, and A.J. Pinz, 1992. Multispectral classification of landsat-images using neural networks, IEEE Trans. on Geoscience and Remote Sensing, 30:492-490.
6. Bruzzone, L., and S.B. Serpico, 1997. An interactive technique for the detection of land-cover transitions in multitemporal remote-sensing images, IEEE Trans. on Geoscience and Remote Sensing, 35(4):858-867.
7. Byrne, G.F., P.F. Crapper, and K.K. Mayo, 1980. Monitoring land-cover change by principal component analysis of multitemporal landsat data, Remote Sensing of Environment, 10:175-184.
8. Chen, C.F., K.S. Chen, and J.S. Chang, 1995. Neural network for change detection of remotely sensed imagery, Proc. SPIE, Image and Signal Processing for Remote Sensing II, Paris, France, 2579:210-215.
9. Civco, D.L., 1993. Artificial neural networks for land-cover classification and mapping, International Journal of Geographic Information Systems, 7(2):173-186.
10. Collins, J.B., and C.E. Woodcock, 1996. An assessment of several linear change detection techniques for mapping forest mortality using multitemporal Landsat TM data, Remote Sensing of Environment, 56:66-77.
11. Coppin, P.R., and M.E. Bauer, 1996. Digital change detection in forest ecosystems with remote sensing imagery, Remote Sensing Reviews, 13:207-234.
12. Dai, X.L., and S. Khorram, 1998a. The effects of image misregistration on the accuracy of remotely sensed change detection, IEEE Trans. on Geoscience and Remote Sensing, 36(5):1566-1577.
13. _, 1998b. Requirements and techniques for an automated change detection system, Proc.IEEE/IGARSS'98, Seattle, Washington, July, pp. 2752-2754.
14. Dai, X.L., S. Khorram, and H.A. Karimi, 1998. A neural network approach to data fusion in remote sensing, Proc. ASPRS Annual Convention (CD-ROM), Tampa, Florida, pp. 191-198.
15. Dreye, P., 1993. Classification of land cover using optimized neural nets on SPOT data, Photogrammetric Engineering & Remote Sensing, 59(5):617-621.
16. Gopal, S., and C. Woodcock, 1996. Remote sensing of forest change using artificial neural networks, IEEE Trans. on Geoscience and Remote Sensing, 34(2):398-404.
17. Kara, Y., R.G. Atkins, S.H. Yueh, R.T. Shin, and J.A. Kong, 1994. Applications of neural networks to radar image classification, IEEE Trans. on Geoscience and Remote Sensing, 32(1):100-109.
18. Heermann, P.D., and N. Khazenie, 1992. Classification of multispectral remote sensing data using a back-propagation neural network, IEEE Trans. on Geoscience and Remote Sensing, 30:81-88.
19. Jacobs, R.A., 1988. Increased rates of convergence through learning rate adaption, Neural Networks, 1(4):295-308.
20. Key, J., J.A. Maslanik, and A.J. Schweiger, 1989. Classification of merged AVHRR and SMMR arctic data with neural networks, Photogrammetric Engineering & Remote Sensing, 55(9):1331-1338.
21. Khorram, S., G.S. Biging, N.R. Chrisman, D.R. Colby, R.G. Congalton, J.E. Dobson, R.L. Ferguson, M.F. Goodchild, J.R. Jensen, and T.H. Mace, 1999. Accuarcy assessment of Remote Sensing Devised Change Detection, ASPRS Monograph, American Society for Photogrammetry & Remote Sensing, Bethesda, Maryland, 64 p.
22. Lambin, E.F., and D. Ehrlich, 1996. The surface temperature-vegetation index space for land cover and land cover change analysis, Int. J. of Remote Sensing, 17(3):463-487.
23. Liu, Z.K., and J.Y. Xiao, 1991. Classification of remotely-sensed image data using artificial neural networks, Int. J. of Remote Sensing, 12(11):2433-2438.
24. Malila, W.A., 1980. Change vector analysis: An approach for detecting forest changes with Landsat, Proceedings of the LARS Machine Processing of Remotely Sensed Data Symposium, West Lafayette, Indiana, pp. 326-336.
25. Michalek, J.L., T.W. Wagner, J.J. Luczkovich, and R.W. Stoffle, 1993. Multispectral change vector analysis for monitoring coastal marine environments, Photogrammetric Engineering & Remote Sensing, 59(3):381-384.
26. Miller, D.M., E.J. Kaminsky, and S. Rana, 1995. Neural network classification of remote-sensing data, Computers & Geosciences, 21(3):377-386.
27. Oliver, C.J., and R.G. White, 1990. Real-time SAR change detection using neural networks, Proc. SPIE, Advanced Signal Processing Algorithms, Architectures, and Implementations, San Diego, California, 1348:40-50.
28. Paola, J.D., and R.A. Schowengerdt, 1995a. A review and analysis of backpropagation neural networks for classification of remotely-sensed multi-spectral imagery, Int. J. of Remote Sensing, 16(16):3033-3056.
29. _, 1995b. A detailed comparison of backpropagation neural network and maximum-likelihood classifiers for urban land use classification, IEEE Trans. on Geoscience and Remote Sensing, 33(4):981-996.
30. Rosin, P.L., 1994. Parcel-based change detection, Proc. SPIE, Image and Signal Processing for Remote Sensing, Rome, Italy, 2315:289-298.
31. Rumelhart, D.E., G.E. Hinton, and R.J. Williams, 1986. Learning internal representations by error propagation, Parallel Distributed Processing: Explorations in the Microstructure of Cognition, Volume 1: Foundations (D.E. Rumelhart and J.L. McClelland, editors), The MIT Press, Cambridge, Massachussetts, pp. 318-362.
32. Schurmann, J., 1996. Pattern Classification: A Unified View of Statistical and Neural Approaches, John Wiley & Sons, New York, 373 p.
33. Singh, A., 1989. Digital change detection techniques using remotely-sensed data, Int. J. of Remote Sensing, 10(6):989-1003.
34. Smith, M., 1993. Neural Networks for Statistical Modeling, Van Nostrand Reinhold, New York.
35. Uberbacher, E.C., Y. Xu, R.W. Lee, C.W. Glover, M. Beckerman, and R.C. Mann, 1996. Image exploitation using multi-sensor/neural network systems, Proc. SPIE, 24th AIPR Workshop on Tools and Techniques for Modeling and Simulation, Washinton, D.C., 2645:134-145.
36. White, R.G., 1991. Change detection in SAR imagery, Int. J. of Remote Sensing, 12(2):339-360.