Skip to main content
SpringerLink
Log in

Fully fuzzy supervised classification of land cover from remotely sensed imagery with an artificial neural network

  • Articles
  • Published:
Neural Computing & Applications Aims and scope Submit manuscript

Abstract

Mixed pixels are a major problem in mapping land cover from remotely sensed imagery. Unfortunately, such imagery may be dominated by mixed pixels, and the conventional ‘hard’ image classification techniques used in mapping applications are unable to appropriately represent the land cover of mixed pixels. Fuzzy classification techniques can, however, accommodate the partial and multiple class membership of mixed pixels, and be used to derive an appropriate land cover representation. This is, however, only a partial solution to the mixed pixel problem in supervised image classification. It must be reognised that the land cover on the ground is fuzzy, at the scale of the pixel, and so it is inappropriate to use procedures designed for ‘hard’ data in the training and testing stages of the classification. Here an approach for land cover classification in which fuzziness is accommodated in all three stages of a supervised classification is presented. Attention focuses on the classification of airborne thematic mapper data with an artificial neural network. Mixed pixels could be accommodated in training the artificial neural network, since the desired output for each training pixel can be specified. A fuzzy land cover representation was derived by outputting the activation level of the network's output units. The activation level of each output unit was significantly correlated with the proportion of the area represented by a pixel which was covered with the class associated with the unit (r>0.88, significant at the 99% level of confidence). Finally, the distance between the fuzzy land cover classification derived from the artificial neural network and the fuzzy ground data was used to illustrate the accuracy of the land cover representation derived. The dangers of hardening the classification output and ground data sets to enable a conventional assessment of classification accuracy are also illustrated; the hardened data sets were over three times more distant from each other than the fuzzy data sets.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Rhind D, Hudson R. Land Use. Methuen, London, 1980.

    Google Scholar 

  2. Estes JE, Mooneyhan DW. Of maps and myths. Photogrammetric Engineering and Remote Sensing 1994; 60: 517–524.

    Google Scholar 

  3. Townshend JRG. Land cover. Int J of Remote Sens 1992; 13: 1319–1328.

    Google Scholar 

  4. Herman JD, Waites JE, Ponitz RM, Etzler P. A temporal and spatial remote sensing study of a Michigan superfund site. Photogrammetric Engineering and Remote Sensing 1994: 60: 1007–1017.

    Google Scholar 

  5. Fuller RM, Groom GB, Jones AR. The land-cover map of Great Britain: An automated classification of Landsat Thematic Mapper Data. Photogrammetric Engineering and Remote Sensing 1994; 60: 553–562.

    Google Scholar 

  6. Stone TA, Schlesinger P, Houghton RA, Woodwell GM. A map of the vegetation of South America based on satellite imagery. Photogrammetric Engineering and Remote Sensing 1994; 60: 541–551.

    Google Scholar 

  7. DeFries RS, Townshend JRG. Global land cover: comparison of ground-based data sets to classification with AVHRR data. In: Foody GM, Curran PJ (eds.), Environmental Remote Sensing from Regional to Global Scales. Wiley, Chichester, 1994, pp. 84–110.

    Google Scholar 

  8. Conese, C, Maselli, F. Use of error matrices to improve area estimates with maximum likelihood classification procedures. Remote Sens of Environ 1992; 40: 113–124.

    Google Scholar 

  9. Benediktsson JA, Swain PH, Ersoy OK. Neural network approaches versus statistical methods in classification of multisource remote sensing data. IEEE Trans Geosci Remote Sens 1990; 28: 540–551.

    Google Scholar 

  10. Peddle DR, Foody GM, Zhang A, Franklin SE, LeDrew EF. Multisource image classification II: an empirical comparison of evidential reasoning and neural network approaches. Canadian J Remote Sens 1994; 20 (4): 397–408.

    Google Scholar 

  11. Wood TF, Foody GM. Analysis and representation of vegetation continua from Landsat Thematic Mapper data for lowland heaths. Int J Remote Sens 1989; 10: 181–191.

    Google Scholar 

  12. Campbell JB. Introduction to Remote Sensing. Guilford, New York, 1987.

    Google Scholar 

  13. Crapper PF. An estimate of the number of boundary cells in a mapped landscape coded to grid cells. Photogrammetric Engineering and Remote Sensing 1984; 50: 1497–1503.

    Google Scholar 

  14. Skoke D, Tucker C. Tropical deforestation and habitat fragmentation in the Amazon: satellite data from 1978 to 1988. Science 1993; 260: 1905–1910.

    Google Scholar 

  15. Wang F. Improving remote sensing image analysis through fuzzy information representation. Photogrammetric Engineering and Remote Sensing 1990; 56: 1163–1169.

    Google Scholar 

  16. Wang F. Fuzzy supervised classification of remote sensing images. IEEE Trans Geosci Remote Sens 1990; 28: 194–201.

    Google Scholar 

  17. Cannon RL, Dave JV, Bezdek JC, Trivedi MM. Segmentation of a thematic mapper image using the fuzzyc-means clustering algorithm. IEEE Trans Geosci Remote Sens 1986; 24: 400–408.

    Google Scholar 

  18. Foody GM, Campbell, NA, Trodd, NM, Wood TF. Derivation and applications of probabilistic measures of class membership from the maximum likelihood classification. Photogrammetric Engineering and Remote Sensing 1992; 58: 1335–1341.

    Google Scholar 

  19. Foody GM. Fuzzy modelling of vegetation from remotely sensed imagery. Ecological Modelling 1996; 85, 3–12.

    Google Scholar 

  20. Metzler MD, Cicone RC. Assessment of technologies for classification of mixed pixels. Proceedings of the Seventeenth International Symposium on Remote Sensing of Environment, vol 3, ERIM, Ann Arbor Michigan, 1983, pp. 1015–1021.

    Google Scholar 

  21. Arai K. A supervised Thematic Mapper classification with a purification of training samples. Int J Remote Sens 1992; 13: 2039–2049.

    Google Scholar 

  22. Schalkoff RJ. Pattern Recognition: Statistical, Structural and Neural Approaches. Wiley, New York, 1992.

    Google Scholar 

  23. Aleksander I, Morton H. An Introduction to Neural Computing. Chapman & Hall, London, 1990.

    Google Scholar 

  24. Davalo E, Naim P. Neural Networks. Macmillan, Basingstoke, 1991.

    Google Scholar 

  25. de Villiers J, Barnard E. Backpropagation neural nets with one and two hidden layers. IEEE Trans Neural Networks 1992; 4: 136–141.

    Google Scholar 

  26. Chauvin Y. Generalization performance of overtrained back-propagation networks, In Almeida LB, Wellekens CJ (eds). Neural Networks. Springer-Verlag, Berlin, 1990, pp. 46–55.

    Google Scholar 

  27. Rumelhart DE, Hinton GE, Williams RJ. Learning internal representation by error propagation. In: Rumelhart DE, McClelland JL (eds.), Parallel Distributed Processing: Explorations in the Microstructure of Cognition: MIT Press, Cambridge MA, 1986, pp. 318–362.

    Google Scholar 

  28. Rosenfield GH, Fitzpatrick-Lins K. A coefficient of agreement as a measure of thematic classification accuracy. Photogrammetric Engineering and Remote Sensing 1986; 52: 223–227.

    Google Scholar 

  29. Congalton RG. A review of assessing the accuracy of classifications of remotely sensed data. Remote Sens Environ 1991: 37: 35–46.

    Google Scholar 

  30. Cohen J. A coefficient of agreement for nominal scales. Educational and Psychological Measurement 1960; 20: 37–46.

    Google Scholar 

  31. Story M, Congalton RG. Accuracy assessment: a user's perspective. Photogrammetric Engineering and Remote Sensing 1986; 52: 397–399.

    Google Scholar 

  32. Congalton RG, Oderwald RG, Mead RA. Assessing Landsat classification accuracy using discrete multivariate analysis statistical techniques. Photogrammetric Engineering and Remote Sensing 1983; 49: 1671–1678.

    Google Scholar 

  33. Gong P, Howarth PJ. The use of structural information for improving land-cover classification accuracies at the rural-urban fringe. Photogrammetric Engineering and Remote Sensing 1990; 56: 67–73.

    Google Scholar 

  34. Cohen J. Weighted kappa. Psychological Bulletin 1968; 70: 213–220.

    Google Scholar 

  35. Foody GM, Trodd NM, Non-classificatory analysis and representation of heathland vegetation from remotely sensed imagery. GeoJournal 1993; 29: 343–350.

    Google Scholar 

  36. Curran PJ, Hay AM. The importance of measurement error for certain procedures in remote sensing at optical wavelengths. Photogrammetric Engineering and Remote Sensing 1986; 52: 229–241.

    Google Scholar 

  37. Curran PJ, Williamson HD. The accuracy of ground data used in remote sensing investigations. Int J Remote Sens 1985; 6: 1637–1651.

    Google Scholar 

  38. Bauer ME, Burk TE, Ek AR, Coppin PR, Lime SD, Walsh TA, Walters DK. Satellite inventory of Minnesota forest resources. Photogrammetric Engineering and Remote Sensing 1994; 60: 287–298.

    Google Scholar 

  39. Gopal S, Woodcock C. Theory and methods for accuracy assessment of thematic maps using fuzzy sets. Photogrammetric Engineering and Remote Sensing 1994; 60: 181–188.

    Google Scholar 

  40. Congalton RG. Accuracy assessment of remotely sensed data: future needs and directions. In Proceedings of Twelfth Pecora Symposium, American Society for Photogrammetry and Remote Sensing, Bethesda, 1994, pp. 385–388.

    Google Scholar 

  41. Finn JT. Use of the average mutual information index in evaluating classification error and consistency. Int J Geographical Infor Syst 1993; 7: 349–366.

    Google Scholar 

  42. Maselli F, Conese C, Petkov L. Use of probability entropy for the estimation and graphical representation of the accuracy of maximum likelihood classifications. ISPRS J Photogrammetry Remote Sens 1994; 49 (2): 13–20.

    Google Scholar 

  43. Klir GJ, Folger TA. Fuzzy Sets, Uncertainty and Information. Prentice-Hall International, London, 1988.

    Google Scholar 

  44. Foody GM. Approaches for the production and evaluation of fuzzy land cover classifications from remotely sensed data. Int J Remote Sens 1996; 17: 1317–1340.

    Google Scholar 

  45. Chang C-I, Chen K, Wang J, Althouse MLG. A relative entropy-based approach to image thresholding. Patt Recogn 1994; 27: 1275–1289.

    Google Scholar 

  46. Higashi M, Klir GJ. On the notion of distance representing information closeness: possibility and probability distributions. Int J General Syst 1983; 9: 103–115.

    Google Scholar 

  47. Kent JT, Mardia KV. Spatial classification using fuzzy membership models. IEEE Trans Pattern Anal Machine Intell 1988; 10: 659–671.

    Google Scholar 

  48. Goodchild MF. Integrating GIS and remote sensing for vegetation analysis and modelling: methodological issues. J Vegetation Sci 1994; 5: 615–626.

    Google Scholar 

  49. Townshend JRG. Agricultural land-cover discrimination using thematic mapper spectral bands. Int J Remote Sens 1984; 6: 681–698.

    Google Scholar 

  50. Foody GM, Cox DP. Sub-pixel land cover composition estimation using a linear mixture model and fuzzy membership functions. Int J Remote Sens 1994; 15: 619–631.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Geography, University of Southampton, SO17 1BJ, Highfield, Southampton, UK

    G. M. Foody

Authors
  1. G. M. Foody
    View author publications

    You can also search for this author in PubMed Google Scholar

Rights and permissions

Reprints and permissions

About this article

Cite this article

Foody, G.M. Fully fuzzy supervised classification of land cover from remotely sensed imagery with an artificial neural network. Neural Comput & Applic 5, 238–247 (1997). https://doi.org/10.1007/BF01424229

Download citation

  • Issue Date:

  • DOI: https://doi.org/10.1007/BF01424229

Keywords

Search

Navigation