An evaluation of the potential for fuzzy classification of multispectral data using artificial neural networks

Photogrammetric Engineering and Remote Sensing

Volumn 63, Issue 11, 1997, Pages 1285-1294

  Warner, Timothy A ^a   Shank, Michael ^b  

^a WEST VIRGINIA UNIVERSITY   (United States)

^b W Virginia Dept Environ Protect   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL NEURAL NETWORK; FUZZY MATHEMATICS; MULTISPECTRAL DATA;

EID: 0031457504     PISSN: 00991112     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (35)

1. Adams, J.B., M.O. Smith, and A.R. Gillespie, 1989. Simple models for complex natural surfaces: A strategy for the hyperspectral era of remote sensing, Proceedings of the IEEE International Geosciences and Remote Sensing Symposium 1989, pp. 16-21.
2. Bateson, A., and B. Curtiss, 1996. A method for manual endmember selection and spectral unmixing, Remote Sensing of Environment, 55:229-243.
3. Benediktsson, J., P. Swain, and O. Ersoy, 1990. Neural network approaches versus statistical methods in classification of multisource remote sensing data, IEEE Transactions on Geoscience and Remote Sensing, 28(4):540-551.
4. Bezdek, J., 1993. Fuzzy models-what are they, and why? IEEE Transactions on Fuzzy Systems, 1(1):1-5.
5. Bischof, H., W. Schneider, and A. Pinz, 1992. Multispectral classification of Landsat images using neural networks, IEEE Transactions on Geoscience and Remote Sensing, 30(3):482-490.
6. Boardman, J., 1994. Geometric mixture analysis of imaging spectrometry data, Proceedings of the IEEE International Geosciences and Remote Sensing Symposium 1994, pp. 2369-2371.
7. Carpenter, G.A., S. Grossberg, N. Markuzon, J.H. Reynolds, and D.B. Rosen, 1992. Fuzzy ART: A neural network architecture for incremental supervised learning of analog multidimensional maps, IEEE Transactions on Neural Networks, 3:598-713.
8. Civco, D., and Y. Wang, 1994. Classification of multispectral, multitemporal, multisource spatial data using artificial neural networks, ASPRS/ACSM Annual Convention & Exposition Technical Papers, Reno, Nevada, 1:123-133.
9. Decatur, S., 1989, Application of neural networks to terrain classification, Proceedings of the 1989 International Joint Conference on Neural Networks, Washington, D.C., 1:283-288.
10. Foody, G.M., 1996. Relating the land-cover composition of mixed pixels to artificial neural network classification output, Photogrammetric Engineering & Remote Sensing, 62(5):491-499.
11. Foody, G.M., N.A. Campbell, N.M. Trodd, and T.F. Wood, 1992. Derivation and applications of probabilistic measures of class membership from the maximum likelihood classification, Photogrammetric Engineering & Remote Sensing, 58(9):1335-1341.
12. Foody, G.M., and D.P. Cox, 1994. Sub-pixel land cover composition estimation using a linear mixture model and fuzzy membership functions, International Journal of Remote Sensing, 15(3):619-631.
13. Foody, G.M., MB. McCulloch, and W.B. Yates, 1995. Classification of remotely sensed data by an artificial neural network: Issues related to training data characteristics, Photogrammetric Engineering & Remote Sensing, 61(4):391-401.
14. Fox, L., A. Fischer, A. Gillespie, and M. Smith, 1990. Using spectral mixture analysis of AVIRIS high dimensional data for distinguishing soil chronosequences, Proceedings of the Second AVIRIS Workshop, JPL Publication 90-54, pp. 94-98.
15. Hepner, G., T. Logan, N. Ritter, and N. Bryant, 1990. Artificial neural network classification using a minimal training set: Comparison to conventional supervised classification, Photogrammetric Engineering & Remote Sensing, 56(4):469-473.
16. Heerman, P., and N. Khazenie, 1992. Classification of multispectral remote sensing data using a back-propagation neural network, IEEE Transactions on Geoscience and Remote Sensing, 30(1):540-551.
17. Key, J., J. Maslanik, and A. Schweiger, 1989. Classification of merged AVHRR and SMMR Arctic data with neural networks, Photogrammetric Engineering & Remote Sensing, 55(9):1331-1338.
18. Kosko, B., 1992. Neural Networks and Fuzzy Systems: A Dynamical Systems Approach to Machine Intelligence, Prentice Hall, Englewood Cliffs, New Jersey, 452 p.
19. Lee, C., and D.A. Landgrebe, 1993. Analyzing high-dimensional multispectral data, IEEE Transactions on Geoscience and Remote Sensing, 31(4):792-800.
20. Lee, J., R. Weger, S. Sengupta, and R. Welch, 1990. A neural network approach to cloud classification, IEEE Transactions on Geoscience and Remote Sensing, 28(5):846-855.
21. Lippmann, R., 1987. An introduction to computing with neural nets, IEEE ASSP Magazine, (April):4-22.
22. Maselli, F., C. Conese, and L. Petkov, 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(2):13-20.
23. Ray, T.W., and B.C. Murray, 1996. Nonlinear spectral mixing in desert vegetation, Remote Sensing of Environment, 55:59-64.
24. Richards, J., 1993. Remote Sensing Image Data Analysis, Springer Verlag, New York, 340 p.
25. Resmini, R.G., M.E. Kappus, W.S. Aldrich, J.C. Harsanyi, and M. Anderson, 1996. Use of Hyperspectral Digital Imagery Collection Experiment (HYDICE) sensor data for quantitative mineral mapping at Cuprite, Nevada, Eleventh Thematic Conference on Applied Geologic Remote Sensing, Las Vegas, Nevada, 27-29 February, 1:48-65.
26. Rich, E., and K. Knight, 1994. Artificial Intelligence, McGraw-Hill, New York, 740 p.
27. Ruck, D.W., S.K. Rogers, M. Kabrisky, M. Oxley, and B. Suter, 1990. The multilayer perception as an approximation to a Bayes discriminant function, IEEE Transactions on Neural Networks, 1(4):296-298.
28. Rumelhart, D., G. Hinton, and R. Williams, 1986. Learning internal representations by error propagation, Parallel Distributed Processing, Volume 1: Foundations (D. Rumelhart and J. McClelland, editors), MIT Press, Cambridge, Massachusetts, pp. 318-362.
29. Schalkoff, R., 1992. Pattern Recognition: Statistical, Structural and Neural Approaches, Wiley, New York, 364 p.
30. Smith, M.O., S.L. Ustin, J.B. Adams, and A.R. Gillespie, 1990. Vegetation in deserts: I. A regional measure of abundance from multispectral images, Remote Sensing of Environment, 31:1-26.
31. Swain, P., and S. Davis, 1978. Remote Sensing: The Quantitative Approach, McGraw Hill, New York, 369 p.
32. Wang, F., 1990, Fuzzy supervised classification of remote sensing images, IEEE Transactions on Geoscience and Remote Sensing, 28(2):194-201.
33. Warner, T.A., D.W. Levandowski, R. Bell, and H. Cetin, 1994. Rule-based geobotanical classification of topographic, aeromagnetic and remotely sensed vegetation community data, Remote Sensing of Environment, 50:41-51.
34. Woodcock, C.E., and A.H. Strahler, 1987. The factor of scale in remote sensing, Remote Sensing of Environment, 21:311-332.
35. Zhuang, X., B. Engel, M. Baumgardner, and P. Swain, 1991. Improving classification of crop residues using digital land ownership data and Landsat TM imagery, Photogrammetric Engineering & Remote Sensing, 57(11):1487-1492.