Automatic Generation of Training Data for Land Use and Land Cover Classification by Fusing Heterogeneous Data Sets

Nowadays, automatic classification of remote sensing data can efficiently produce maps of land use and land cover, which provide an essential source of information in the field of environmental sciences. Most state-of-the-art algorithms use supervised learning methods that require a large amount of annotated training data. In order to avoid time-consuming manual labelling, we propose a method for the automatic annotation of remote sensing data that relies on available land use and land cover information. Using the example of automatic labelling of SAR data, we show how the Dempster-Shafer evidence theory can be used to fuse information from different land use and land cover products into one training data set. Our results confirm that the combination of information from OpenStreetMap, CORINE Land Cover 2018, Global Surface Water and the SAR data itself leads to reliable class assignments, and that this combination outperforms each considered single land use and land cover product.