Approaches for the production and evaluation of fuzzy land cover classifications from remotely-sensed data

International Journal of Remote Sensing

Volumn 17, Issue 7, 1996, Pages 1317-1340

  Foody, G M ^a  

^a UNIVERSITY OF SALFORD   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; FUZZY SETS; LANDFORMS; MAPPING; MEMBERSHIP FUNCTIONS; NEURAL NETWORKS; PATTERN RECOGNITION;

CLASS MEMBERSHIP; FUZZY LAND COVER CLASSIFICATION; LAND COVER; STATISTICAL CLASSIFICATION;

REMOTE SENSING;

CLASSIFICATION ACCURACY; DISCRIMINANT ANALYSIS; FUZZY CLASSIFICATION; LAND COVER MAPPING; NEURAL NETWORK; REMOTE SENSING DATA;

EID: 0030135691     PISSN: 01431161     EISSN: 13665901     Source Type: Journal    
DOI: 10.1080/01431169608948706     Document Type: Article

References (76)

1. Aleksander, I., and Morton, H., 1990, An Introduction to Neural Computing (London: Chapman and Hall).
2. Altman, D., 1994, Fuzzy set theoretic approaches for handling imprecision in spatial analysis. International Journal of Geographical Information Systems, 8, 271-289.
3. Aronoff, S., 1986, The minimum accuracy value as an index of classification accuracy. Photogrammetric Engineering and Remote Sensing, 51, 593-600.
4. Atkinson, P. M., 1996, Scale and spatial dependence. In Scaling-up, edited by P. R. van Gardingen, G. M. Foody and P. J. Curran (Cambridge: Cambridge University Press) (in press).
5. Bauer, M. E., Burk, T. E., Ek, A. R., Coppin, P. R., Lime, S. D., Walsh, T. A., and Walters, D. K., 1994, Satellite inventory of Minnesota forest resources. Photogrammetric Engineering and Remote Sensing, 60, 287-298.
6. Benediktsson, J. A., Swain, P. H., and Ersoy, O. K., 1990, Neural network approaches versus statistical methods in classification of multisource remote sensing data. I.E.E.E. Transactions on Geoscience and Remote Sensing, 28, 540-551.
7. Bezdek, J, C., 1981, Pattern Recognition with Fuzzy Objective Functions (New York: Plenum Press).
8. Bezdek, J. C., 1993, Fuzzy models-what are they, and why? I.E.E.E. Transactions on Fuzzy Systems, 1, 1-6.
9. Bezdek, J. C., Ehrlich, R., and Full, W., 1984, FCM: the fuzzy c-means clustering algorithm. Computers and Geosciences, 10, 191-203.
10. Bosserman, R. W., and Ragade, R. K., 1982, Ecosystem analysis using fuzzy set theory. Ecological Modelling, 16, 191-208.
11. Campbell, J. B., 1987, Introduction to Remote Sensing (New York: Guilford Press).
12. Cannon, R. L., Dave, J. V., Bezdek, J. C., and Trivedi, M. M., 1986, Segmentation of a thematic mapper image using the fuzzy c-means clustering algorithm. I.E.E.E. Transactions on Geoscience and Remote Sensing, 24, 400-408.
13. Chang, C-I., Chen, K., Wang, J., and Althouse, M. L. G., 1994, A relative entropy-based approach to image thresholding. Pattern Recognition, 27, 1275-1289.
14. Cohen, J., 1960, A coefficient of agreement for nominal scales. Educational and Psychological Measurement, 20, 37-46.
15. Cohen, J., 1968, Weighted kappa. Psychological Bulletin, 70, 213-220.
16. Conesh, C., and Maselli, F., 1993, Selection of optimum bands from TM scenes through mutual information analysis. ISPRS Journal of Photogrammetry and Remote Sensing, 48, 2-11.
17. Congalton, R. G., 1991, A review of assessing the accuracy of classifications of remotely sensed data. Remote Sensing of Environment, 37, 35-46.
18. Congalton, R. G., 1994, Accuracy assessment of remotely sensed data: future needs and directions. Pecora 12: Land Information from Space-Based Systems (Bethesda: American Society for Photogrammetry and Remote Sensing), pp. 385-388.
19. Congalton, R. G., Oderwald, R. G., and Mead, R. A., 1983, Assessing Landsat classification accuracy using discrete multivariate analysis statistical techniques. Photogrammetric Engineering and Remote Sensing, 49, 1671-1678.
20. Corves, C., and Place, C. J., 1994, Mapping the reliability of satellite-derived landcover maps-an example from central Brazilian Amazon Basin. International Journal of Remote Sensing, 15, 1283-1294.
21. Crapper, P. F., 1984, An estimate of the number of boundary cells in a mapped landscape coded to grid cells. Photogrammetric Engineering and Remote Sensing, 50, 1497-1503.
22. Curran, P. J., and Williamson, H. D., 1985, The accuracy of ground data used in remote sensing investigations. International Journal of Remote Sensing, 6, 1637-1651.
23. Curran, P. J., and Hay, A. M., 1986, The importance of measurement error for certain procedures in remote sensing at optical wavelengths. Photogrammetric Engineering and Remote Sensing, 52, 229-241.
24. DeFries, R. S., and Townshend, J. R. G., 1994, Global land cover: comparison of ground-based data sets to classifications with AVHRR data. In Environmental Remote Sensing from Regional to Global Scales, edited by G. M. Foody and P. J. Curran (Chichester: Wiley), pp. 84-110.
25. Estes, J. E., and Mooneyhan, D. W., 1994, Of maps and myths. Photogrammetric Engineering and Remote Sensing, 60, 517-524
26. Finn, J. T., 1993, Use of the average mutual information index in evaluating classification error and consistency. International Journal of Geographical Information Systems, 7, 349-366.
27. Fischer, M. M., and Gopal, S., 1993, Neurocomputing-a new paradigm for geographic information processing. Environment and Planning A, 23, 757-760.
28. Fisher, P. F., 1996, The pixel: a snare and a delusion. In Proceedings of Environmental GIS and Remote Sensing, edited by P. Pan (Nottingham: Remote Sensing Society) (in press).
29. Fisher, P. F., and Pathirana, S., 1990, The evaluation of fuzzy membership of land cover classes in the suburban zone. Remote Sensing of Environment, 34, 121-132.
30. Foody, G. M., 1992, Classification accuracy assessment: some alternatives to the kappa coefficient for nominal and ordinal level classifications. Remote Sensing from Research to Operation (Nottingham, Remote Sensing Society), pp. 529-538.
31. Foody, G. M., 1995 a, Cross-entropy for the evaluation of the accuracy of a fuzzy land cover classification with fuzzy ground data. ISPRS Journal of Photogrammetry and Remote Sensing, 50, 2-12.
32. Foody, G. M., 1995 b, Fully fuzzy supervised image classification. RSS '95: Remote Sensing in Action (Nottingham: Remote Sensing Society), pp. 1187-1194.
33. Foody, G. M., 1996, Fuzzy modelling of vegetation from remotely sensed imagery. Ecological Modelling, 85, 3-12.
34. Foody, G. M., Campbell, N. A., Trodd, N. M., and Wood, T. F., 1992, Derivation and applications of probabilistic measures of class membership from the maximum likelihood classification. Photogrammetric Engineering and Remote Sensing, 58, 1335-1341.
35. Foody, G. M., and Trodd, N. M., 1993. Non-classificatory analysis and representation of heathland vegetation from remotely sensed imagery. GeoJournal, 29, 343-350.
36. Foody, G. M., and Cox, D. P., 1994, Sub-pixel land cover composition estimation using a linear mixture model and fuzzy membership functions. International Journal of Remote Sensing, 15, 619-631.
37. Foody, G. M., McCulloch, M. B., and Yates, W. B., 1995, Classification of remotely sensed data by an artificial neural network: issues related to training data characteristics. Photogrammetric Engineering and Remote Sensing, 61, 391-401.
38. Goodchild, M. F., 1994, Integrating GIS and remote sensing for vegetation analysis and modelling: methodological issues. Journal of Vegetation Science, 5, 615-626.
39. Gong, P., and Howarth, P. J., 1990. The use of structural information for improving land-cover classification accuracies at the rural-urban fringe. Photogrammetric Engineering and Remote Sensing, 56, 67-73.
40. Gopal, S., and Woodcock, C., 1994, Theory and methods for accuracy assessment of thematic maps using fuzzy sets. Photogrammetric Engineering and Remote Sensing, 60, 181-188.
41. Hall, G.B., Wang, F., and Subaryono S., 1992, Comparison of Boolean and fuzzy classification methods in land suitability analysis using a geographical information system. Environment and Planning A, 24, 497-516.
42. Harris, R., 1985, Contextual classification post-processing of Landsat data using a probabilistic relaxation model. International Journal of Remote Sensing, 6, 847-866.
43. Hay, A. M., 1979, Sampling designs to test land-use map accuracy. Photogrammetric Engineering and Remote Sensing, 45, 529-533.
44. Higashi, M., and Klir, G. J., 1983, On the notion of distance representing information closeness: possibility and probability distributions. International Journal of General Systems, 9, 103-115.
45. Hisdal, E., 1994, Interpretative versus prescriptive fuzzy set theory. I.E.E.E. Transactions on Fuzzy Systems, 2, 22-26.
46. Janssen, L. V., and van der Wel, F. J. M., 1994, Accuracy assessment of satellite derived land-cover data: a review. Photogrammetric Engineering and Remote Sensing, 60, 419-426.
47. Kanellopoulos, I., Varfis, A., Wilkinson, G. G., and Megier, J., 1992, Land-cover discrimination in SPOT HRV imagery using an artificial neural network-a 20-class experiment. International Journal of Remote Sensing, 13, 917-924.
48. Kent, J. T., and Mardia, K. V., 1988, Spatial classification using fuzzy membership models. l.E.E.E. Transactions on Pattern Analysis and Machine Intelligence, 10, 659-671.
49. Key, J. R., Maslanik, J. A., and Barry, R. G., 1989, Cloud classification from satellite data using a fuzzy sets algorithm: a polar example. International Journal of Remote Sensing, 10, 1823-1842.
50. Klecka, W. R., 1980, Discriminant Analysis (Beverly Hills, CA: Sage).
51. Klir, G. J., 1994, On the alleged superiority of probabilistic representation of uncertainty. I.E.E.E. Transactions on Fuzzy Systems, 2, 27-31.
52. Klir, G. J., and Folger, T. A., 1988, Fuzzy Sets. Uncertainty and Information (London: Prentice-Hall International).
53. Lark, R. M., 1994, Sample size and class variability in the choice of a method of discriminant analysis. International Journal of Remote Sensing, 15, 1551-1555.
54. Maselli, F, Conese, C., and Petkov, L., 1994, Use of probability entropy for the estimation and graphical representation of the accuracy of maximum likelihood classifications. ISPRS Journal of Photogrammetry and Remote Sensing, 49, 13-20.
55. Mather, P. M, 1987, Computer Processing of Remotely-Sensed Images (Chichester: Wiley).
56. McBratney, A. B., and Moore, A. W., 1985, Application of fuzzy sets to climatic classification. Agricultural and Forest Meteorology, 35, 165-185.
57. Moon, W. M., 1993, On mathematical representation and integration of multiple spatial geoscience datasets. Canadian Journal of Remote Sensing, 19, 63-67.
58. Pal, N. R., and Bezdek, J. C., 1994, Measuring fuzzy uncertainty. l.E.E.E. Transactions on Fuzzy Systems, 2, 107-118.
59. Peddle, D. R., 1993, An empirical comparison of evidential reasoning, linear discriminant analysis and maximum likelihood algorithms for land cover classification. Canadian Journal of Remote Sensing, 19, 31-44.
60. Rhind, D., and Hudson, R., 1980, Land Use (London: Methuen).
61. Rosenfield, G. H., and Fitzpatrick-Lins, K., 1986, A coefficient of agreement as a measure of thematic classification accuracy. Photogrammetric Engineering and Remote Sensing, 52, 223-227.
62. Rumelhart, D. E., Hinton, G. E., and Williams, R. J., 1986, Learning internal representation by error propagation. In Parallel Distributed Processing: Explorations in the Microstructure of Cognition, edited by D. E. Rumelhart and J. L. McClelland (Cambridge MA: MIT Press), pp. 318-362.
63. Schalkoff, R. J., 1992, Pattern Recognition: Statistical. Structural and Neural Approaches, (New York: Wiley).
64. Srinivasan, A., and Richards, J. A., 1990, Knowledge-based techniques for multi-source classification. International Journal of Remote Sensing, 11, 505-525.
65. Story, M., and Congalton, R. G., 1986, Accuracy assessment: a user’s perspective, Photogrammetric Engineering and Remote Sensing, 52, 397-399.
66. Thomas, I. L., Benning, V. M., and Ching, N. P., 1987, Classification of Remotely Sensed Images, (Bristol: Adam Hilger).
67. Tom, C. H., and Miller, L. D., 1984, An automated land-use mapping comparison of the Bayesian maximum likelihood and linear discriminant analysis algorithms. Photogrammetric Engineering and Remote Sensing, 50, 193-207.
68. Townshend, J. R. G., 1992, Land cover. International Journal of Remote Sensing, 13, 1319-1328.
69. Townshend, J. R. G., 1984, Agricultural land-cover discrimination using thematic mapper spectral bands. International Journal of Remote Sensing, 6, 681-698.
70. Townshend, J. R. G., and Justice, C. O., 1981, Information extraction from remotely-sensed data: a user view. International Journal of Remote Sensing, 2, 313-329.
71. Townshend, J, Justice, C., Li, W., Gurney, C., and McManus, J., 1991, Global land cover classification by remote sensing: present capabilities and future possibilities. Remote Sensing of Environment, 35, 243-255.
72. Wang, F., 1990a, Improving remote sensing image analysis through fuzzy information representation. Photogrammetric Engineering and Remote Sensing, 56, 1163-1169.
73. Wang, F., 1990 b, Fuzzy supervised classification of remote sensing images. l.E.E.E. Transactions on Geoscience and Remote Sensing, 28, 194-201.
74. Wang, F., 1994, The use of artificial neural networks in a geographical information system for agricultural land-suitability assessment. Environment and Planning A, 26, 265-284.
75. Wang, Y., and Civco, D. L., 1992, Post-classification of misclassified pixels by evidential reasoning: a G1S approach for improving classification accuracy of remote sensing data. Proceedings ISPRS Conference 1992 (Washington DC: ISPRS), pp. 80-86.
76. Wood, T. F., and Foody, G. M., 1989, Analysis and representation of vegetation continua from Landsat Thematic Mapper data for lowland heaths. International Journal of Remote Sensing, 10, 181-191.