The aim of this research was to develop a methodology that can automate the sugar cane mapping task when remote sensing data are used. For this, we tested the integration of two major approaches of Artificial Intelligence: Object Based Image Analysis (OBIA) and Data Mining (DM). The study area comprises the municipalities of Ipuã, Guará and São Joaquim da Barra, located in the northwestern of São Paulo state, which are well representatives of the conditions of agriculture in southern and southeastern regions of Brazil. OBIA was used to emulate the interpreter knowledge in the process of sugar cane mapping, and MD techniques were employed for automatic generation of knowledge model. MD algorithm used was C4.5, which generates decision trees (DT) from a previous prepared training set. A time series of Landsat images was acquired in order to represent the wide patterns variability within the sugar cane crop season. The objects were generated by application of multiresolution segmentation algorithm. Thereafter, the knowledge extraction process has begun, which ends with the acquisition of DT. Once properly trained, the DT was applied to the Landsat time series and then generated the thematic map. Classification accuracy was then assessed using error matrix analysis, Kappa statistics, and tests for statistical significance, indicating that the examined classification routines achieved an overall accuracy of 94% and Kappa of 0,87. The results shows that OBIA and MD are very efficient and promising in the direction of automating the sugar cane classification process.Pages: 467-47
