Classification of High-Resolution Remote-Sensing Image Using OpenStreetMap Information

Prior information about classes plays an important role in the high-resolution image classification. Produced by volunteers with GPS tracking practice and local knowledge, the crowdsourced OpenStreetMap (OSM) data have shown potential as a time-saving and cost-effective way to provide prior information for image classification. In this letter, we develop a high-resolution remote-sensing image classification method using OSM information. OSM objects of classes of interest except roads are extracted to construct the training set for classification. To decrease the misleading errors and redundancy in OSM, a series of approaches is employed successively to refine the training set. Furthermore, OSM road information is directly superimposed on the learned classification result owing to its good quality and completeness. The main contributions of this letter are: 1) the refinement of OSM-derived training samples and 2) the utilization of OSM road superimposition strategy. The high-resolution GF-2 image over the Guangzhou peri-urban area as well as the corresponding OSM data is employed in the experiments. The results illustrate the effectiveness of the proposed method.