DOI QR코드

DOI QR Code

Change Detection in Land-Cover Pattern Using Region Growing Segmentation and Fuzzy Classification

  • Lee Sang-Hoon (Department of Industrial Engineering, Kyungwon University)
  • Published : 2005.02.01

Abstract

This study utilized a spatial region growing segmentation and a classification using fuzzy membership vectors to detect the changes in the images observed at different dates. Consider two co-registered images of the same scene, and one image is supposed to have the class map of the scene at the observation time. The method performs the unsupervised segmentation and the fuzzy classification for the other image, and then detects the changes in the scene by examining the changes in the fuzzy membership vectors of the segmented regions in the classification procedure. The algorithm was evaluated with simulated images and then applied to a real scene of the Korean Peninsula using the KOMPSAT-l EOC images. In the expertments, the proposed method showed a great performance for detecting changes in land-cover.

Keywords

References

  1. Anderberg, M. R, 1973. Cluster Analysis for Application, Academic Press, NY
  2. Bruzzone, L. and D. F. Prieto, 2000. Automatic analysis of the difference image for unsupervised change detection, IEEE Trans. Geosci. Remote Sensing, 38: 1171-1182 https://doi.org/10.1109/36.843009
  3. Bruzzone, L. and D. F. Prieto, 2002. An adaptive semiparametric and context-based approach to unsupervised change detection in multitemporal remote sensing images IEEE Trans. Image Processing, 11: 452-466 https://doi.org/10.1109/TIP.2002.999678
  4. Dai, X. and S. Khorram, 1998. The effects of image misregistration on the accuracy of remotely sensed change detection, IEEE Trans. Geosci. Remote Sensing, 61(3): 313-320
  5. Gong, P., E. F. LeDrew, and J. R. Miller, 1992. Registration-noise reduction in difference images for change detection, Int. J. Remote Sens., 13(4): 773-779 https://doi.org/10.1080/01431169208904151
  6. Lee, S-H., 1990. An Unsupervised Hierarchical Clustering Image Segmentation and an Adaptive Image Reconstruction System for Remote Sensing, Ph. D. Dissertation, University of Texas at Austin
  7. Lee, S-H., 2001. Unsupervised image classification using spatial region growing segmentation and hierarchical clustering, Korean Journal of Remote Sensing, 17(1):57-70 (in Korean) https://doi.org/10.7780/kjrs.2001.17.1.57
  8. Lee, S-H., 2004a. Unsupervised image classification using region-growing segmentation based on CN-chain, Korean Journal of Remote Sensing, 20(3): 235-248 (in Korean) https://doi.org/10.7780/kjrs.2004.20.4.235
  9. Lee, S-H., 2004b. Fuzzy training based on segmentation using spatial region growing, Korean Journal of Remote Sensing, 20(5), 353-359 https://doi.org/10.7780/kjrs.2004.20.5.353
  10. Lee, S-H., 2005. Image classification based on hierarchical clustering and fuzzy membership vector, Korean Journal of Remote Sensing, accepted
  11. Liang, Z, R. J. Jaszczak and R. E. Coleman, Parameter Estimation of Finite Mixture Using the EM Algorithm and Information Criteria with Application to Medical Image Processing, IEEE Trans. Nucl. Sci., Vol. 39, 1992, pp1126-1133 https://doi.org/10.1109/23.159772
  12. Singh, A., 1989. Digital change detection techniques using remotely-sensed data, Int J. Remote Sens., 10: 989-1003 https://doi.org/10.1080/01431168908903939
  13. Zeng, Y., J. Zhang, and G. Wang, 2002. Change detection of guildings using high resolution remotely sensed data, Proc. Int. Sym. Remote Sensing, pp.530-535