Integration of remote sensing, GIS and expert knowledge in national knowledge-based crop recognition in Mediterranean environment

This paper describes a knowledge-based crop recognition system, integrating remote sensing analysis and geographical information in hierarchical way. Landsat TM and Spot pan images were merged to reduce heterogeneity by enhancing field boundaries. Multi-temporal NDVI maps generated from these images were classified into eight crop types using unsupervised classification algorithm. Using agricultural knowledge, the relations between natural vegetation and crop types, spectral and phenological properties and precipitation and soil types were engineered. These relations were used as binary rules in an experimental knowledge-based crop recognition system. The binary rules were activated by iterative “split-and-merge” mechanism of the mixed unsupervised classification clusters aimed at refining the map products given by the application of unsupervised classification algorithm alone. Experiments in a wide region of Israel using ground data from the Israeli Agriculture Ministry have shown that the use of knowledge-based “split-and-merge” rules gives improvement of approximately 9% compared to the unsupervised classification alone.