Semantic segmentation using deep neural networks for SAR and optical image pairs

Kurzfassung

Semantic segmentation for synthetic aperture radar (SAR) imagery is a rarely touched area, due to the specific image characteristics of SAR images. In this research, we propose a dataset which consists of three data sources: TerraSAR-X images, Google Earth images and OpenStreetMap data, with the purpose of performing SAR and optical image semantic segmentation. By using fully convolutional networks and deep residual networks with pre-trained weights, we investigate the accuracy and mean IOU values of semantic segmentation for both SAR and optical image patches. The best Segmentation accuracy results for SAR and optical data are around 74% and 82%. Moreover, we study SAR models by combining multiple data sources: Google Earth images and OpenStreetMap data.