The KIPS Transactions:PartD (정보처리학회논문지D)

Korea Information Processing Society (한국정보처리학회)
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Land Surface Classification With Airborne Multi-spectral Scanner Image Using A Neuro-Fuzzy Model

뉴로-퍼지 모델을 이용한 항공다중분광주사기 영상의 지표면 분류

  • 한종규 (한국지질자원연구원 국가지질자원정보센터) ;
  • 류근호 (충북대학교 전기전자 및 컴퓨터공학부 컴퓨터정보통신연구소) ;
  • 연영광 (충북대학교 대학원 전자계산학과) ;
  • 지광훈
  • Published : 2002.10.01
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Abstract

In this paper, we propose and apply new classification method to the remotely sensed image acquired from airborne multi-spectral scanner. This is a neuro-fuzzy image classifier derived from the generic model of a 3-layer fuzzy perceptron. We implement a classification software system with the proposed method for land cover image classification. Comparisons with the proposed and maximum-likelihood classifiers are also presented. The results show that the neuro-fuzzy classification method classifies more accurately than the maximum likelihood method. In comparing the maximum-likelihood classification map with the neuro-fuzzy classification map, it is apparent that there is more different as amount as 7.96% in the overall accuracy. Most of the differences are in the "Building" and "Pine tree", for which the neuro-fuzzy classifier was considerably more accurate. However, the "Bare soil" is classified more correctly with the maximum-likelihood classifier rather than the neuro-fuzzy classifier.

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References

  1. F. Maselli, C. Conese, G. Zipoli, and M. A. Pittau, Use of Error Probabilities to Improve Area Estimates Based on Maximum Likelihood Classification, Remote Sensing of Environment, Vol.31, pp.155-160, 1990 https://doi.org/10.1016/0034-4257(90)90063-R
  2. P. M. Mather, Computer Processing of Remotely Sensed Images, Chichester, Wiley, 1987
  3. W. M. Moon, On mathematical representation and integration of multiple spatial geoscience datasets, Canadian Journal of Remote Sensing, Vol.19, pp.63-67, 1993 https://doi.org/10.1080/07038992.1993.10855151
  4. D. R. Peddle, An empirical comparison of evidential reasoning, linear discriminant analysis and maximum likelihood algorithms for land cover classification, Canadian Journal of Remote Sensing, Vol.19, pp.31-44, 1993 https://doi.org/10.1080/07038992.1993.10855148
  5. R. L. Cannon, J. V. Dave, J. C. Bezdek, and M. M. Trivedi, Segmentation of a thematic mapper image using the fuzzy c-rneans clustering algorithm, IEEE Trans. on Geoscience and Remote Sensing, Vol.24, pp.400-408, 1986 https://doi.org/10.1109/TGRS.1986.289598
  6. N. D. Ritter and G. F. Hepner, Application of an artificial neural network to land cover classification of thematic mapper imagery, Computer Geosci., Vol.16, pp.873-880, 1990 https://doi.org/10.1016/0098-3004(90)90009-I
  7. H. Ishibuchi, K. Nozaki, and H. Tanaka, Distributed representation of fuzzy rules and its application to pattern classification, Fuzzy Sets and System, Vol.52, pp.21- 32, 1992 https://doi.org/10.1016/0165-0114(92)90032-Y
  8. S. K. Halgamuge and M. Glesner., Neural networks in designing fuzzy systems for real world applications, Fuzzy Sets and Systems, Vol.65, pp.1-12, 1994 https://doi.org/10.1016/0165-0114(94)90242-9
  9. R. E. Roger, Sparse inverse covariance matrices and efficient maximum likelihood classification of hyperspectral data, Int. J. Remote Sensing, Vol.17, No.3, pp.589-613, 1996 https://doi.org/10.1080/01431169608949029
  10. R. R. Yager and D. P. Filev. Generation of fuzzy rules by mountain clustering, Journal of Intelligent and Fuzzy Systems, Vol.2, pp.209-219, 1994