The spectral-temporal response surface and its use in the multi-sensor, multitemporal classification of agricultural crops

International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives

Volumn 33, Issue , 2000, Pages 582-589

  Vieira, Carlos ^a   Mather, Paul ^a   McCullagh, Michael ^a  

^a UNIVERSITY OF NOTTINGHAM   (United Kingdom)

Author keywords

Classification; Integration; Land use; Multisensor multispectral fusion; Multitemporal

Indexed keywords

CLASSIFICATION (OF INFORMATION); CROPS; IMAGE ENHANCEMENT; INTEGRATION; LAND USE; MAXIMUM LIKELIHOOD; NEURAL NETWORKS; REMOTE SENSING;

CLASSIFICATION ACCURACY; FUNCTION INTERPOLATION; INFORMATION RELIABILITY; MULTI SENSOR; MULTI-TEMPORAL; MULTITEMPORAL CLASSIFICATION; REMOTELY SENSED DATA; SUPERVISED CLASSIFIERS;

DATA HANDLING;

EID: 84973859749     PISSN: 16821750     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (27)

1. Badhwar, G. D., 1984. Classification of corn-soybean using multitemporal Thematic Mapper data. Remote Sensing of Environment, 16, pp. 175-182.
2. Badhwar, G. D., Autin, W. W., and Carnes, J. G., 1982. A semi-automatic for multitemporal classification of a given crop within a Landsat scene. Pattern Recognition, 15, pp. 217-230.
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 Transactions on Geoscience and Remote Sensing, 28, pp. 540-552.
4. Bischof, H., Schneider, W., and Pinz, A. J., 1992. Multispectral classification of Landsat-images using neural networks. IEEE Transactions on Geoscience and Remote Sensing, 30, pp. 482-490.
5. Burrough, P. A., 1986. Principles of Geographical Information Systems for Land Resources Assessment. Oxford: Clarendon Press.
6. Civco, D. L., 1993. Artificial neural networks for land-cover classification and mapping. International Journal of Geographic Information Systems, 7, pp. 173-183.
7. Congalton, R. G., and Green, K., 1998. Assessing the Accuracy of Remotely Sensed Data: Principles and Practices. New York: Lewis Publishers.
8. Davis, J. C., 1973. Statistics and Data Analysis in Geology. New York: John Wiley and Sons.
9. Haralick, R. M., Hlavka, C. A, Yokoyama, R., and Carlyle, S.M., 1980. Spectral-temporal classification using vegetation phenology. IEEE Transactions on Geoscience and Remote Sensing, 18, pp. 167-174.
10. Hardy, R. L., 1971. Multiquadric equations of topography and other irregular surfaces. Journal of Geophysical Research, 43, pp. 475- 492.
11. Hirose, Y., Yamashita, K., and Hijiya, S., 1991. Back-propagation algorithm which varies the number of hidden units. Neural Networks, 4, pp. 61-66.
12. Kanellopoulos, I., Wilkinson, G. G., Roli, F., and Austin, J. (eds.), 1997. Neurocomputation in Remote Sensing Data Analysis. Berlin: Springer-Verlag.
13. Lam, N., 1983. Spatial Interpolation Methods: a Review. The American Cartographer, 10, pp. 129-149.
14. Lambin, E. F. and Strahler, A. H., 1994(a). Change-vector analysis in multitemporal space: a tool to detect and categorise land-cover change processes using high temporal-resolution satellite data. Remote Sensing of Environment, 48, pp. 231-244.
15. Lambin, E. F. and Strahler, A. H., 1994(b). Indicators of land-cover change for change-vector analysis in multitemporal space at coarse spatial scales. International Journal of Remote Sensing, 15, pp. 2099-2119.
16. Lippmann, R. P., 1987. An introduction to computing with neural nets. IEEE ASSP Magazine, April, pp. 4-22.
17. Jensen, J. R., 1986. Introductory Digital Image Processing - A Remote Sensing Perspective. Englewood Cliffs, NJ: Prentice-Hall.
18. Mather, P. M., 1975. The use of orthogonal polynomials in least square problems. Computer Applications in the Natural and Social Sciences, New Series, 2(10), Department of Geography, University of Nottingham.
19. Mather, P. M., 1976. Computational Methods of Multivariate Analysis in Physical Geography. Chichester: John Wiley and Sons.
20. Mather, P. M., 1999. Computer Processing of Remotely-Sensed Images: An Introduction. Chichester: John-Wiley and Sons, Second edition.
21. Olsson, H., 1995. Radiometric Calibration of Thematic Mapper data for forest change detection. International Journal of Remote Sensing, 24, pp. 81-96.
22. Ortiz, M. J., Formaggio, A. R., and Epiphano, J. C. N., 1997. Classification of croplands through integration of remote sensing, GIS and historical database. International Journal of Remote Sensing, 18, pp. 95-105.
23. Paola, J. D., and Schowengerdt, R. A., 1995. A review and analysis of backpropagation neural networks for classification of remotelysensed multispectral imagery. International Journal of Remote Sensing, 16, pp. 3033-3058.
24. Schut, G. H., 1976. Review of interpolation methods for digital terrain models. The Canadian Surveyor, 30, pp. 389-412.
25. Tou, J. T. and Gonzalez, R.C., 1974. Pattern Recognition Principles. Reading, Massachusetts: Addison - Wesley.
26. Tso, B.C.K., 1997. An Investigation of Alternative Strategies for Incorporating Spectral, Textural, and Contextual Information in Remote Sensing Image Classification. Unpublished Ph.D. thesis, Department of Geography, The University of Nottingham, pp. 44-53.
27. Watson, D. F., 1996. Contouring A Guide to the Analysis and Display of Spatial Data. Oxford: Pergamon Press.