Korean Journal of Remote Sensing (대한원격탐사학회지)

Korean Society of Remote Sensing (대한원격탐사학회)
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Integration of Multi-spectral Remote Sensing Images and GIS Thematic Data for Supervised Land Cover Classification

  • Jang Dong-Ho (National Research Laboratory (Harmful Algal Blooming Control), Kongju National University) ;
  • Chung Chang-Jo F (Geological Survey of Canada)
  • Published : 2004.10.01
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Abstract

Nowadays, interests in land cover classification using not only multi-sensor images but also thematic GIS information are increasing. Often, although useful GIS information for the classification is available, the traditional MLE (maximum likelihood estimation techniques) does not allow us to use the information, due to the fact that it cannot handle the GIS data properly. This paper propose two extended MLE algorithms that can integrate both remote sensing images and GIS thematic data for land-cover classification. They include modified MLE and Bayesian predictive likelihood estimation technique (BPLE) techniques that can handle both categorical GIS thematic data and remote sensing images in an integrated manner. The proposed algorithms were evaluated through supervised land-cover classification with Landsat ETM+ images and an existing land-use map in the Gongju area, Korea. As a result, the proposed method showed considerable improvements in classification accuracy, when compared with other multi-spectral classification techniques. The integration of remote sensing images and the land-use map showed that overall accuracy indicated an improvement in classification accuracy of 10.8% when using MLE, and 9.6% for the BPLE. The case study also showed that the proposed algorithms enable the extraction of the area with land-cover change. In conclusion, land cover classification results produced through the integration of various GIS spatial data and multi-spectral images, will be useful to involve complementary data to make more accurate decisions.

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References

  1. Benediktsson, J. A. and J. R. Sveinsson, 1997. Feature extraction for multisource data classification with artificial neural networks, International Journal of Remote Sensing, 18(4): 727-740
  2. Binaghi, E., P. Madella, M. G. Montesano, and A. Rampini, 1997. Fuzzy contextual classification of multisource remote sensing images, IEEE Transactions on Geoscience and Remote Sensing, 35: 326-340
  3. Chou P. B. and C. M. Brown, 1988, Multimodal reconstruction and segmentation with Markov Random Fields and HCF ptimization, 1988 DARPA Image Understanding Workshop, pp.214-221
  4. Chung, C. F., A. G. Fabbri, and C. J. van Westen, 1995. Multivariate regression analysis for landslide hazard zonation, Geographical Infonnation Systems in Assessing Natural Hazards (A. Carrara and F. Guzzetti, editors), Kluwer Academic Publishers, Dordrecht, The Netherlands, pp.107-133
  5. Clerici, A., S. Perego, C. Tellini, and P. Vescovi, 2002. A procedure for landslide susceptibility zonation by the conditional analysis method, Geomorphology 48: 349-364
  6. Dai, A. and S. Khorram, 1999. Data fusion using artificial neural networks: a case study on multitemporal change analysis, Computer, Environment and Urban Systems 23: 19-31
  7. Franklin, S. E., D. R. Peddle, J. A. Dechka, and G. B. Stenhouse, 2002. Evidential reasoning with Landsat TM, DEM and GIS data for land cover classification in support of grizzly bear habitat mapping, International Journal of Remote Sensing, 23(21): 4633-4652
  8. Hegarat-Mascle, S. L., I. Bloch, and D. VidaI-Madjar，1997. Application of Dempster-Shafer evidence theory to unsupervised classification in multisource remote sensing, IEEE Transactions on Geoscience and Remote Sensing, 35: 1018-1031
  9. Hubert-Moy, L., A. Cotonnec, L. Le Du, A. Chardin, and P. Perez, 2001. A Comparison of Parametric Classification Procedures of Remotely Sensed Data Applied on Different Landscape Units, Remote Sensing of Environment, 75: 174-187
  10. Johnson, N. L. and S. Kotz, 1972. Distributions in Statistics: Continuous Multivariate Distribution, Yew York. Wiley
  11. McLallen, G. J., 1990. Discriminant analysis and statistical pattern recognition, John Wiley & Sons, INC
  12. Peddle, D. R., 1995. MERCURY(: an evidential reasoning image classifier, Computers & Geosciences, 21(10):1163-1176
  13. Peddle, D. R. and D. T. Ferguson, 2002. Optimisation of multisource data analysis: an example using evidential reasoning for GIS data classification, Computers and Geoscience, 28: 45-52
  14. Press, S. J., 1972. Applied multivariate analysis, Holt, Rinehart and Winston, INC., New York
  15. Richards, J. A., 1995. Remote sensing digital image analysis: An introduction, 2nd Edition, New York, Springer-Verlag
  16. Richards, J. A., D. A. Landgrebe, and P. H. Swain, 1982. A means for utilizing ancillary information in multispectral classification, Remote Sensing of Environment, 12: 463-477
  17. Serpico, S. B., L. Bruzzone, and F. Roli, 1996. An experimental comparison of neural and statistical nonparametric algorithms for supervised classification of remote-sensing images, Pattern Recognition Letters, 17: 1331-1341
  18. Smits P. C. and S. G. Dellepiane, 1997. Synthetic aperture radar image segmentation be a detail preserving Markov random field approach, IEEE Transactions on Geoscience and Remote Sensing, 35: 844-857
  19. Solaiman, B., L. E. Pierce, and F. T. Ulaby, 1999. Multisensor data fusion using fuzzy concepts: application to land cover classification using ERS-1/IJERS-1 SAR composites, IEEE Transactions on Geoscience and Remote Sensing, 37: 1316-1326
  20. Solberg, A. H. S., 1999. Contextual data fusion applied to forest map revision, IEEE Transactions on Geoscience and Remote Sensing, 37(3): 1234-1243
  21. Solberg, A. H. S., T. Taxt, and A. K. Jain, 1996. A Markov random field model for classification of multisource satellite imagery, IEEE Transactions on Geoscience and Remote Sensing, 34(1): 100-113 https://doi.org/10.1109/36.481897
  22. Tso, B. C. K. and P. M. Mather, 1999. Classification of multisource remote sensing imagery using a genetic algorithm and Markov random fields, IEEE Transactions on Geoscience and Remote Sensing, 37: 1255-1260
  23. Warrender, C. and M. Augusteijn, 1999. Fusion of image classifications using Bayesian techniques with Markov random fields, International Journal of Remote Sensing, 20(10): 1987-2002
  24. Wright, W. A., 1989. A Markov random field approach to data fusion and color segmentation, 1989, Image Vis. Comput., 7: 144-150