Korean Journal of Remote Sensing (대한원격탐사학회지)

Korean Society of Remote Sensing (대한원격탐사학회)
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Adaptive Reconstruction of Harmonic Time Series Using Point-Jacobian Iteration MAP Estimation and Dynamic Compositing: Simulation Study

  • Lee, Sang-Hoon (Department of Industrial Engineering, Kyungwon University)
  • Published : 2008.02.28
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Abstract

Irregular temporal sampling is a common feature of geophysical and biological time series in remote sensing. This study proposes an on-line system for reconstructing observation image series contaminated by noises resulted from mechanical problems or sensing environmental condition. There is also a high likelihood that during the data acquisition periods the target site corresponding to any given pixel may be covered by fog or cloud, thereby resulting in bad or missing observation. The surface parameters associated with the land are usually dependent on the climate, and many physical processes that are displayed in the image sensed from the land then exhibit temporal variation with seasonal periodicity. A feedback system proposed in this study reconstructs a sequence of images remotely sensed from the land surface having the physical processes with seasonal periodicity. The harmonic model is used to track seasonal variation through time, and a Gibbs random field (GRF) is used to represent the spatial dependency of digital image processes. The experimental results of this simulation study show the potentiality of the proposed system to reconstruct the image series observed by imperfect sensing technology from the environment which are frequently influenced by bad weather. This study provides fundamental information on the elements of the proposed system for right usage in application.

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References

  1. Carlotto, M. J., 1985. Techniques for multispectral image classification, SPIE Digital Image Processing, 528: 174-191.
  2. Carlotto, M. J., 1997. Detection and analysis of change in remotely sensed imagery with application to wide area surveillance, IEEE Trans. Image Processing, 6: 189-202. https://doi.org/10.1109/83.552106
  3. Cullen, C. G., 1972. Matrices and Linear Transformations. Reading, MA: Addison- Wesley.
  4. Fung, T., 1990. An assessment of TM imagery for land-cover change detection, IEEE Trans. Geosci. Remote Sensing, 28: 681-684. https://doi.org/10.1109/TGRS.1990.572980
  5. Georgii, H. O., 1979. Canonical Gibbs Measure. Berlin, Germany: Springer-Verlag.
  6. Goldberg, M., G. Karem, and M. Alvo, 1983. A production rule-based expert system for interpreting multi-temporal LANDSAT imagery, CVPR'83 Proceedings.
  7. Horvath, N. C., T. I. Grey, and D. G. McCray, 1982. Advanced Very High Resolution Radiometer (AVHRR) data evaluation for use in monitoring vegetation, AgRISTAR Report EW-L@-040303, JSC-18243, NASA, Lyndon B. Johnson Space Center, Houston, TX., 1982.0-1365.
  8. Jeon, B. and D. A. Landgrebe, 1999. Decision fusion approach for multitemporal classification, IEEE Trans. Geosci. Remote Sens., 37: 1227- 1233. https://doi.org/10.1109/36.763278
  9. Khazenie, N. and M. M. Crawford, 1990. Spatialtemporal autocorrelated model for contextual classification, IEEE Trans. Geosci. Remote Sens., 28: 529-539. https://doi.org/10.1109/TGRS.1990.572942
  10. Lee, S. and M. M. Crawford, 1991. Adaptive reconstruction system for spatially correlated multispectral multitemporal images, IEEE Trans. on Geosci. Remote Sens., 29: 494-503. https://doi.org/10.1109/36.135811
  11. Lee, S-H, 2002. Reconstruction and Change Monitoring of Image Series, Korean Journal of Remote Sensing, 18: 157-170 (Korean version).
  12. Lee, S-H, 2007. Speckle Removal of SAR Imagery Using a Point-Jacobian Iteration MAP Estimation, Korean Journal of Remote Sensing, 23: 33-42. https://doi.org/10.7780/kjrs.2007.23.1.33
  13. Melgani, F. and S. B. Serpico, 2003. A Markov random field approach to spatio-temporal contextual image classification, IEEE Trans. Geosci. Remote Sens., 41: 2478-2487. https://doi.org/10.1109/TGRS.2003.817269
  14. Tucker, C. J., N. B. Hoblen, J. H. Elgin, Jr, and J. E. Mcmurtey III, 1990. Relationship of spectral data to grain yield variation, Photogramm. Eng. Remote Sens., 46: 657-666.
  15. Teng, W. L., 1990. AVHRR monitoring of US. Crops during the 1988 drought, Photogramm. Eng. Remote Sens., 56: 1143-1146.
  16. Tarpley, J. D., S. R. Schneider, and R. L. Money, 1984. Global vegetation indices from the NOAA-7 meteorological satellite, J. Climate Appl. Meteorol., 23: 491-494. https://doi.org/10.1175/1520-0450(1984)023<0491:GVIFTN>2.0.CO;2
  17. Singh, A., 1989. Digital change detection techniques using remotely sensed data, Int. J. Remote Sensing, 10: 989-1003. https://doi.org/10.1080/01431168908903939
  18. Townsend, J. R. G. and C. J. Tucker, 1984. Objective assessment of AVHRR data for land cover mapping, Int. J. Remote Sens., 5: 492-501.
  19. Townshend, J. R. G. and C. O. Justice, 1995. Spatial variability of imagesand the monitoring of changes in the normalized difference vegetation index, Int. J. Remote Sensing, 16(12): 2187-2195. https://doi.org/10.1080/01431169508954550