Fusion of multi-temporal high resolution optical image series and crop rotation information for land-cover map production

The generation of land-cover maps for agriculture is a recurrent problem in remote sensing. There exist many efficient algorithms, but they often need well selected images during specific periods, which delays the map availability to the end of the season. In this work, we propose to introduce prior knowledge about the crop rotation in order to both improve the classification and obtain an accurate map early in the year. We use a Bayesian Network to model the crop rotation and we introduce the output of the model into a Support Vector Machine classifier to generate a land-cover map. We evaluate the overall improvement and the effect on several crops.