Korean Journal of Remote Sensing (대한원격탐사학회지)

Korean Society of Remote Sensing (대한원격탐사학회)
권호 표지
DOI QR코드

DOI QR Code

Evidential Fusion of Multsensor Multichannel Imagery

  • Lee Sang-Hoon (Department of Industrial Engineering, Kyungwon University)
  • Published : 2006.02.01
Download PDF
⟨ Previous Next ⟩

Abstract

This paper has dealt with a data fusion for the problem of land-cover classification using multisensor imagery. Dempster-Shafer evidence theory has been employed to combine the information extracted from the multiple data of same site. The Dempster-Shafer's approach has two important advantages for remote sensing application: one is that it enables to consider a compound class which consists of several land-cover types and the other is that the incompleteness of each sensor data due to cloud-cover can be modeled for the fusion process. The image classification based on the Dempster-Shafer theory usually assumes that each sensor is represented by a single channel. The evidential approach to image classification, which utilizes a mass function obtained under the assumption of class-independent beta distribution, has been discussed for the multiple sets of mutichannel data acquired from different sensors. The proposed method has applied to the KOMPSAT-1 EOC panchromatic imagery and LANDSAT ETM+ data, which were acquired over Yongin/Nuengpyung area of Korean peninsula. The experiment has shown that it is greatly effective on the applications in which it is hard to find homogeneous regions represented by a single land-cover type in training process.

Keywords

References

  1. Abidi, M. and R. Gonzalez, 1992. Data Fusion in Robotics and Machine Intelligence, Academic Press, NY
  2. Bloch, I., 1996. Some aspects of Dempster-Shafer evidence theory for classification of multimodality medical images taking partial volume effect into account, Pattern Recognition Letters, 17(8): 905-919 https://doi.org/10.1016/0167-8655(96)00039-6
  3. Barnet, J. A., 1991. Calculating Demspter-Shafer plausibility, IEEE Trans. Pattern Anal. Machine Intell., 13(1): 599-602 https://doi.org/10.1109/34.87345
  4. Buede, D. M. and P. Girardi, 1997. A target identification comparison of Bayesian and Dempster-Shafer Multisensor Fusion, IEEE Trans. Syst., Man, Cybern., 27(5): 569-577 https://doi.org/10.1109/3468.618256
  5. Dempster, A. P., 1968. A generalization of Bayesian inference, J. Royal Statisit. Soc. B., 30(2): 205-247
  6. Jouan, A. and Y. Allard, 2004. Land use mapping with evidential fusion of features extracted from polarimetric synthetic aperture radar and hyperspectral imagery, Information Fusion, 5(4): 251-267 https://doi.org/10.1016/j.inffus.2003.10.005
  7. Le Hegarat-Mascle, S., I. Bloch, and D. Vidal-Madjar, 1997. Application of Dempster-Shafer evidence theory to unsupervised classification in multisource remote sensing, IEEE Trans. Geosci. Remote Sens., 35(4): 1018-1031 https://doi.org/10.1109/36.602544
  8. Lee, S-H., 2001. Unsupervised image classification using spatial region growing segmentation and hierarchical clustering, Korean J. Remote Sensing, 17(1): 57-70 (in Korean)
  9. Lee, S-H., 2003. Analysis of land-cover type using multistage hierarchical clustering image classification, Korean J. Remote Sensing, 19(2): 135-147 (in Korean)
  10. Lee, S-H., 2004. Dempster-Shafer fusion of multisensor imagery using Gaussian mass function, Korean J. Remote Sensing, 20(6): 419-425 (in Korean)
  11. Lee, T., J. A. Richards, and P. H. Swain, 1987. Probabilistic and evidential approaches for multisource data analysis, IEEE Trans. Geosci. Remote Sensing, 25(2): 283-293 https://doi.org/10.1109/TGRS.1987.289800
  12. Oguamanam, D. C. D., H. R. Martin, and J. P. Huissoon, 1995. On the application of the beta distribution to gear Damage analysis, Applied Acoustics, 45: 247-261 https://doi.org/10.1016/0003-682X(95)00001-P
  13. Saizenstein, F. and A. Q. Boudraa, 2004. Iterative estimation of Dempster-Shafer's basic probability assignment: application to multisensor image segmentation, SPIE, 43: 1293-1299 https://doi.org/10.1117/1.1737373
  14. Shafer, G., 1976. A Mathematical Theory of Evidence, Princeton University Press, NJ
  15. Pieczynski, W., 2000. Unsupervised Dempster-Shafer fusion of dependent sensors, Proc. 4th IEEE Southwest Symp. Image Analysis and Interpretation, pp. 247-251
  16. Taxt, A. and A. H. S. Solberg, 1997. Information fusion in remote sensing, Vistas in Astronomy, 41(3): 337-342 https://doi.org/10.1016/S0083-6656(97)00036-6
  17. Tupin, F., I. Bloch, and H. Maitre, 1999. A first step toward automatic interpretation of SAR images using evidential fusion of several structure detectors, IEEE Trans. Geosci Remote Sensing, 37: 1327-1343 https://doi.org/10.1109/36.763297
  18. van Cleynenbreugel, J., S. Osinga, F. Fierens, P. Suetens, and A. Oosterlinck, 1991. Road extraction from multitemporal satellite images by an evidential reasoning approach, Pattern Recognition Letters, 12: 371-380 https://doi.org/10.1016/S0167-8655(05)80007-8
  19. Wald, L., 1999. Some terms of reference in data fusion. IEEE Trans. Geosci. Remote Sensing, 37(3): 1190-1193 https://doi.org/10.1109/36.763269