Artificial neural network response to mixed pixels in coarse-resolution satellite data

Remote Sensing of Environment

Volumn 58, Issue 3, 1996, Pages 329-343

  Moody, Aaron ^a   Gopal, Sucharita ^b   Strahler, Alan H ^b  

^a University of North Carolina at Chapel Hill   (United States)

^b Univ of North Carolina ^*

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; BACKPROPAGATION; COMPUTER SIMULATION; DATA PROCESSING; EQUIVALENCE CLASSES; ERROR ANALYSIS; HEURISTIC METHODS; NEURAL NETWORKS;

COARSE RESOLUTION SATELLITE DATA; SUBPIXEL CLASS COMPOSITION;

REMOTE SENSING;

CLASSIFICATION; LANDSAT TM; MAP ACCURACY; NEURAL NETWORK; PIXEL; RESOLUTION; SATELLITE DATA;

EID: 0030300901     PISSN: 00344257     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0034-4257(96)00107-1     Document Type: Article

References (29)

1. Abuelgasim, A. A., Gopal, S., Irons, J. R., and Strahler, A. H. (forthcoming), Classification of ASAS multiangle and multispectral measurements using artificial neural networks, Remote Sens. Environ. 57:79-87.
2. Adams, J. B., Smith, M. O., and Johnson, P. E. (1986), Spectral mixture modeling: A new analysis of rock and soil types at the Viking Lander 1 site, J. Geophys. Res. 91(B8):8098-8112.
3. Benediktsson, J. A., Swain, P. H., and Ersoy, O. K. (1990), Neural network approaches versus statistical methods in classification of multisource remote sensing data, IEEE Trans. Geosci. Remote Sens. 28:540-552.
4. Birdle, J. S. C. (1989), Probabilistic interpretation of feedforward classification network outputs with relationships to statistical pattern recognition, in Neuro-Computing: Algorithms, Architectures and Applications (F. Fougelman-Soulie and J. Herault, Eds.), Springer-Verlag, New York, pp. 227-236.
5. Carpenter, G. A., Grossberg, S., Markuzon, N., Reynolds, J. H., and Rosen, D. B. (1992), Fuzzy ART: a neural network architecture for incremental supervised learning of analog multidimensional maps, IEEE Trans. Neural Net. 3:698-713.
6. Civco, D. L. (1993), Artificial neural networks for land-cover classification and mapping. International Journal of Geographical Information Systems. 7(2):173-186.
7. Congalton, R. (1986), Accuracy assessment: a user's perspective. Photogramm. Eng. Remote Sens. 52(3):397-399.
8. Dreyer, P. (1993), Classification of land cover using optimized neural nets on SPOT data. Photogramm. Eng. Remote Sens. 59(5):617-621.
9. Fisher, P. (1994), Visualization of the reliability in classified remotely sensed images, Photogramm. Eng Remote Sens. 57(7):905-910.
10. Fisher, P., and Pathirana, S. (1990), An evaluation of fuzzy membership of landcover classes in the suburban zone, Remote Sens. Environ. 34:121-132.
11. 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. Photogramm. Eng. Remote Sens. 58(9): 1335-1341.
12. Gibbons, J. D. (1976), Nonparametric Methods for Quantitative Analysis, Holt, Rinehart and Winston, New York.
13. Gopal, S., and Woodcock, C. E. (1994), Theory and methods for accuracy assessment of thematic maps using fuzzy sets, Photogramm. Eng. Remote Sens. 60(2):181-188.
14. Gopal, S., Sklarew, D., and Lambin, E. (1994), Fuzzy-neural networks in multi-temporal classification of land-cover change in the Sahel, in Proceedings of the DOSES Workshop on New Tooh for Spatial Analysis, November 18-20, Lisbon, Portugal, pp. 55-68.
15. Hepner, G. F., Logan, T., Ritter, N., and Bryant, N. (1990), Artificial neural network classification using a minimal training set: comparison to conventional supervised classification, Photogramm. Eng Remote Sens. 56(4):469-473.
16. Hewitson, B., and Crane, R. (1994), Looks and uses, in Neural Nets: Applications in Geography (B. Hewitson and R. Crane, Eds.), Kluwer Academic, Dordrecht, pp. 1-9.
17. Kanellopoulos, I., Varfis, A., Wilkinson, G., and Megier, J. (1992), Land-cover discrimination in SPOT HRV imagery using an artificial neural network - a 20-class experiment, Int. J. Remote Sens, 13:917-924.
18. Key, J. (1994), Classification of arctic cloud and sea ice features in multispectral satellite data, in Neural Nets: Applications in Geography (B. Hewitson and R. Crane, Eds.), Kluwer Academic, Dordrecht, pp. 145-179.
19. Key, J., Maslanik, J. A., and Schweiger, A. J. (1989), Classification of merged AVHRR and SMMR arctic data with neural networks, Photogramm. Eng. Remote Sens. 55(9): 1331-1338.
20. Lee, J., Weger, R. C., Sengupta, S. K., and Welch, R. M. (1990), A neural approach to cloud classification, IEEE Trans. Geosci. Remote Sens. 28:846-855.
21. Richards, J. A. (1993), Remote Sensing Digital Image Analysis: An Introduction, 2nd ed., Springer-Verlag, Berlin, 194 pp.
22. Roberts, D. A., Smith, M. O., and Adams, J. B. (1993), Green vegetation, nonphotosynthetic vegetation, and soils in AVIRIS data, Remote Sens. Environ. 44:222-269.
23. Robinson, V. B., and Thongs, D. (1985), Fuzzy set theory applied to the mixed pixel problem of multispectral landcover databases, in Geographic Information Systems in Government, (B. Opitz, Ed.), pp. 871-876.
24. Ruck, D., Rogers, S., Kabrisky, M., Oxley, M., and Suter, B. (1990), The multilayer perception as an approximation to the Bayes optimal discriminant function, IEEE Trans. Neural Net. 1(4): 296-298.
25. Rumelhart, D. E., Hinton, G. E., and Williams, R. J. (1986), Learning internal representation by error propagation, in Parallel Distributed Processing: Explorations in the Microstructures of Cognition (D. E. Rumelhart and J. L. McClelland, Eds.), MIT Press, Cambridge, MA, pp. 318-362.
26. Running, S., Justice, C., Hall, D., et al. (1994), Terrestrial remote sensing science and algorithms planned for EOS/ MODIS, Int. J. Remote Sens. 15:1453-1463.
27. Strahler, A., Moody, A., Lambin, E., et al. (1994), MODIS land cover product algorithm technical basis document, NASA document for MODIS Product No. 11, Parameter Nos. 2669 and 2671.
28. Woodcock, C. E., Collins, J., Gopal, S., et al. (1994a), Mapping forest vegetation using Landsat TM imagery and a canopy reflectance model, Remote Sens. Environ. 50:240-254.
29. Woodcock, C. E., Gopal, S., Macomber, S. A., and Jakabhazy, V. D. (1994b), Accuracy assessment of the vegetation map of the Plumas National Forest, Technical Report, Center for Remote Sensing, Boston University, 19 pp.