Enhancing Building Shape Details Through Deep Learning in Single-Image SAR-Based DSM

Due to the reliability of data acquisition, synthetic aperture radar (SAR) sensors are fundamental for remote sensing applications with the need for flexibility and fast response. For urban applications, besides the analysis of salient point signatures, extracted height information allows to evaluate the state of buildings. Recently developed deep learning approaches enable height estimates in situations where only one SAR image of an area of interest is available. However, building shapes still exhibit low quality in the resulting digital surface models (DSMs). This paper presents how derived surface models from the SAR image can be refined with knowledge about the shape of buildings. For that purpose, building representations are learned with a neural network from optical images and CityGML models. The results demonstrate that our model not only effectively transfers knowledge to process DSMs from various data sources but also showcases the ability to generalize across different regions.