AN EXPERIMENTAL SYSTEM FOR THE INTEGRATION OF GIS DATA IN KNOWLEDGE-BASED IMAGE-ANALYSIS FOR REMOTE-SENSING OF AGRICULTURE

This paper describes a knowledge-based system which has been developed for integrating easily-available geographical context information from a GIS in remotely-sensed image analysis. An experiment is described in which soil maps and buffered road networks have been used as additional data layers for classifying single date SPOT images for estimates of crop acreages. The map datasets have been digitised, co-registered to the satellite imagery, and manipulated using ARC/INFO. The knowledge base consists of both image context rules and geographical context rules. Probabilistic information from the image classifier and from the rule base is combined using the Dempster-Shafer model of evidential reasoning. Tests using ground data from the Departement Loir-et-Cher, France, have shown that use of the knowledge-based system with GIS data gives an accuracy improvement of approximately 13 per cent compared to using a parametric image classifier alone.