Journal of KIISE:Computing Practices and Letters (한국정보과학회논문지:컴퓨팅의 실제 및 레터)

Korean Institute of Information Scientists and Engineers (한국정보과학회)
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Supervised Classification Systems for High Resolution Satellite Images

고해상도 위성영상을 위한 감독분류 시스템

  • 전영준 (동의대학교 컴퓨터공학과) ;
  • 김진일 (동의대학교 컴퓨터공학과)
  • Published : 2003.06.01
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초록

In this paper, we design and Implement the supervised classification systems for high resolution satellite images. The systems support various interfaces and statistical data of training samples so that we can select the m()st effective training data. In addition, the efficient extension of new classification algorithms and satellite image formats are applied easily through the modularized systems. The classifiers are considered the characteristics of spectral bands from the selected training data. They provide various supervised classification algorithms which include Parallelepiped, Minimum distance, Mahalanobis distance, Maximum likelihood and Fuzzy theory. We used IKONOS images for the input and verified the systems for the classification of high resolution satellite images.

본 논문에서는 고해상도 위성영상의 효과적인 분류를 위한 감독분류 시스템을 설계하고 구현하였다. 구현된 시스템은 분류의 정확도 향상을 위한 훈련데이타의 효율적인 선택을 위해서 다양한 인터페이스와 통계자료를 제공한다. 또한, 다양한 위성영상 포맷의 지원과 새로운 감독분류 알고리즘의 확장을 용이하게 하기 위하여 시스템을 모듈화 하였으며, 분광 특성을 고려한 분류의 적용이 가능하다. 분류 알고리즘으로는 평행육면체 분류, 최소거리 분류, 마하라노비스 거리 분류, 최대우도 분류, 퍼지 분류의 감독분류기법을 이용하여 고해상도 위성영상의 처리를 지원한다. 본 시스템의 적용은 고해상도 IKONOS 위성영상을 입력으로 하고, 그 결과를 분석하여 봄으로써 시스템의 응용 가능성을 보여준다.

키워드

참고문헌

  1. John A. Richards, Remote Sensing Digital Image Analysis : An Introduction, Second, Revised and Enlarged Edition, pp. 229-262, Springer-Verlag, 1994
  2. R. Schowengerdt, Techniques of Image Processing and Classification in Remote Sensing, 1st Ed., pp. 1-58, Academic Press, 1983
  3. Jimenez L.O. and Landgrebe D.A., 'Supervised classification in high-dimensional space: geometrical, statistical, and asymptotical properties of multivariate data Systems,' Man, and Cybernetics, Part C, Applications and Reviews, IEEE Transactions on, Volume 28 Issue 1, pp. 39-54, 1998 https://doi.org/10.1109/5326.661089
  4. Hoffbeck, Joseph P. and David A. Landgrebe, 'Classification of Remote Sensing Images having High Spectral Resolution,' Remote Sensing of Environment, Vol. 57, No.3, pp, 119-126, September 1996 https://doi.org/10.1016/0034-4257(95)00138-7
  5. David Landgrebe, Information Extraction Principles and Methods for Multispectral and Hyperspectral Image Data, Chapter 1 of Information Processing for Remote Sensing, edited by C. H. Chen, published by the World Scientific Publishing Co., Inc., pp. 1-30, Spring, 1999
  6. Pierce L., Samples G., Dobson M.C. and Ulaby, F., 'An automated unsupervised/supervised classification methodology,' Geoscience and Remote Sensing Symposium Proceedings, IGARSS '98. 1998 IEEE International, Vol. 4, pp. 1781-1783, 1998 https://doi.org/10.1109/IGARSS.1998.703650
  7. Jain, A.K., Duin, P.W. and Jianchang Mao, 'Statistical pattern recognition: a review,' Pattern Analysis and Machine Intelligence, IEEE Transactions on, Vol. 22 Issue 1, pp. 4-37, 2000 https://doi.org/10.1109/34.824819
  8. Richard A Johnson, Dean W. Wichern, Applied Multivariate Statistical Analysis, Prentice Hall, pp. 573-627, 1992
  9. Gorte, B. and Stein, A, 'Bayesian classification and class area estimation of satellite images using stratification, ' Geoscience and Remote Sensing, IEEE Transactions on, Vol. 36 Issue 3, pp. 803-812, 1998 https://doi.org/10.1109/36.673673
  10. Melgani, F., Hashemy B.A.R. and Taha S.M.R. 'An explicit fuzzy supervised classification method for multispectral remote sensing images,' Geoscience and Remote Sensing, IEEE Transactions on, Vol. 38 Issue 1 Part 1, pp. 287-295, 2000 https://doi.org/10.1109/36.823921
  11. Wang, F., 1990, 'Fuzzy Supervised Classification of Remote Sensing Images,' IEEE Trans. on Geosicience and Remote Sensing, Vol. 28, No. 2, pp. 194-201, 1990 https://doi.org/10.1109/36.46698
  12. Ishibushi, H., K.Nozaki and H.Tanaka, 'Distributed representation of fuzzy rules and its application to pattern classification,' Fuzzy Sets and Systems Vol. 52, pp. 21-32, 1992 https://doi.org/10.1016/0165-0114(92)90032-Y
  13. Nakashiim T., Nakai G. and Ishibuchi, H., 'Improving the performance of fuzzy classification systems by membership function learning and feature selection,' Fuzzy Systems, FUZZ-IEEE '02. Proceedings of the 2002 IEEE International Conference on, Vol. 1, pp. 488-493, 2002 https://doi.org/10.1109/FUZZ.2002.1005039
  14. Saldju Tadjudin and David A. Landgrebe, 'Covariance Estimation For Limited Training Samples,' IEEE International Geoscience and Remote Sensing Symposium, Seattle, W A July 6-10, pp. 2688-2690, 1998 https://doi.org/10.1109/IGARSS.1998.702320