Detecting subpixel woody vegetation in digital imagery using two artificial intelligence approaches

Photogrammetric Engineering and Remote Sensing

Volumn 63, Issue 5, 1997, Pages 493-500

  Foschi, Patricia G ^a   Smith, Deborah K ^a  

^a SAN FRANCISCO STATE UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; CLASSIFICATION; IMAGE ACCURACY; MACHINE VISION; NEURAL NETWORK; REMOTE SENSING; SATELLITE IMAGERY; SPOT; VEGETATION MAPPING; WOODY VEGETATION;

UK;

EID: 0030613485     PISSN: 00991112     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (24)

1. Argialas, D.P., 1990. Knowledge-based image interpretation: techniques and applications, Technical Papers of ACSM/ASPRS Annual Convention, Denver, Colorado, 4:33-42.
2. Baudry, J., 1984. Effects of landscape structure on biological communities: the case of hedgerow network landscapes, Proceedings of 1st International Seminar on Methodology in Landscape Ecological Research and Planning, Roskilde, Denmark, International Association for Landscape Ecology, 1:55-65.
3. Benediktsson, J.A., P.H. Swain, and O.K. Ersoy, 1990. Neural network approaches versus statistical methods in classification of multisource remote sensing data, IEEE Transactions on Geoscience and Remote Sensing, GE-28:540-552.
4. Boggess, J.E., 1994. Using artificial neural networks to identify roads in satellite images, Proceedings of the World Congress on Neural Networks, San Diego, California, 1:410-415.
5. California Scientific Software, 1992. BrainMaker Neural Network Simulation Software: User's Guide and Reference Manual, Nevada City, California.
6. Dayhoff, J.E., 1990. Neural Network Architectures: An Introduction, Van Nostrand Reinhold, New York, N.Y.
7. Dreyer, P., 1993. Classification of land cover using optimized neural nets on SPOT data, Photogrammetric Engineering & Remote Sensing, 59:617-621.
8. Enslin, W.R., J. Ton, and A. Jain, 1987. Land cover change detection using a GIS-guided, feature-based classification of Landsat Thematic Mapper data, Technical Papers of ASPRS/ACSM Annual Convention, Baltimore, Maryland, 6:108-120.
9. Forman, R.T.T., and M. Godron, 1986. Landscape Ecology, John Wiley and Sons, New York, N.Y.
10. Foschi, P.G., 1992. Classification of Subpixel Woody Vegetation in Remotely-Sensed Imagery of Lowland Britain, doctoral thesis, University of Oxford, UK.
11. _, 1994. A geometric approach to a mixed pixel problem: detecting subpixel woody vegetation, Remote Sensing of Environment, 50:317-327.
12. Hammerstrom, D., 1993. Working with neural networks, IEEE Spectrum, 30(7):46-53.
13. Heermann, P.D., and N. Khazenie, 1992. Classification of multispectral remote sensing data using a back-propagation neural network, IEEE Transactions on Geoscience and Remote Sensing, GE-30:81-88.
14. Hepner, G.F., 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, 56:469-473.
15. Howald, K.J., 1989. Neural network image classification, Technical Papers of ASPRS/ACSM Fall Convention, Cleveland, Ohio, pp. 207-215.
16. Kanellopoulos, I., A. Varfis, G.G. Wilkinson, and J. Megier, 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.
17. Key, J., A.J. Schweiger, and J.A. Maslanik, 1990. Mapping sea ice leads with a coupled numeric/symbolic system, Technical Papers of ACSM/ASPRS Annual Convention, Denver, Colorado, 4: 228-237.
18. Lawrence, J., 1993. Introduction to Neural Networks: Design, Theory, and Applications, California Scientific Software, Nevada City, California.
19. Mather, P.M., 1987. Computer Processing of Remotely-Sensed Images: An Introduction, John Wiley and Sons, Chichester, UK.
20. McClellan, G.E., R.N. DeWitt, T.H. Hemmer, L.N. Matheson, and G.O. Moe, 1989. Multispectral image-processing with a three-layer backpropagation network, Proceedings of International Joint Conference on Neural Networks, Piscataway, New Jersey, IEEE, 1:151-153.
21. Nagao, M., and T. Matsuyama, 1980. A Structural Analysis of Complex Aerial Photographs, Plenum, New York, N.Y.
22. Penn, B.S., A.J. Gordon, and R.F. Wendlandt, 1993. Using neural networks to locate edges and linear features in satellite images, Computers and Geosciences, 19:1545-1565.
23. Rumelhart, D.E., and J.L. McClelland, 1986. Parallel Distributed Processing, MIT Press, Cambridge, Massachusetts, Vols. 1-2.
24. Ryan, T.W., P.J. Sementilli, P. Yuen, and B.R. Hunt, 1991. Extraction of shoreline features by neural nets and image processing, Photogrammetric Engineering & Remote Sensing, 57:947-955.