An Information Fusion Approach of UAV and Satellite Data for Intra Field Classification

Intra field classification of agriculture fields is challenging using dual pol sentinel 1 data due to its limited spatial information. With the use of sensors mountable on unmanned aerial vehicles (UAV) that has a fine spatial resolution, it is easy to obtain intra-field information. However, the use of drones is expensive and their coverage is limited. Therefore, an information fusion approach is used to make intelligent use of high-resolution drone and freely available Sentine1-1 data to provide intra-field classification at large scale for precision agriculture monitoring. Supervised random forest, support vector machine and k-nearest neighborhood classifiers were trained using the polarimetric parameters obtained from dual pol Sentine1-1 data for the classification of sparse and dense vegetation in a large sugarcane field. The overall accuracy of the proposed method is above 84% in segregating sparse and dense sugarcane fields.