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.

Investigating the synergy of deep learning and high-resolution Synthetic Aperture Radar (SAR) data, this paper focuses on building footprint extraction – a domain traditionally dominated by optical imagery. The proposed method involves projecting SAR data onto a digital terrain model and utilizing a modified U-Net for segmenting the building outlines in a common projected map system. An extensive data set consisting of TerraSAR-X images and OpenStreetMap building footprints was created to train the model. With the very promising results, the study positions SAR as a reliable alternative for accurate building footprint mapping, with implications for time-critical disaster management and urban monitoring.

The deep learning-based estimation of topographic heights from single remote sensing images has shown great potential in recent years. Drawing inspiration from the computer vision task of single image depth estimation, the translation from the input remote sensing image to a height image via convolutional neural networks lies at the core of the approaches published so far. Most of the existing works, however, neglect the fact that remote sensing data comes from well-calibrated sensors carried by satellites flying in well-controlled orbits. Thus, a lot of high-quality meta-information is available for most remote sensing images, which can be used to provide the pure deep neural network with physically meaningful auxiliary information. This holds particularly for synthetic aperture radar (SAR) sensors, which use active imaging technology and are thus largely independent from external conditions. In this paper, we investigate whether the inclusion of the radar viewing angle, which is a critical sensor parameter in SAR imaging, provides a benefit for deep learning-based single-image height estimation from VHR SAR data.

Automated change detection is certainly one of the most discussed applications of remote sensing. However, existing approaches rely on finely co-registered images, as otherwise differences in view point or illumination could lead to erroneous change detections. This is particularly true for very-high-resolution sensors, for which even minor differences might affect several resolution cells. In this paper, we make use of deep learning-based single image height prediction to transform highly non-similar synthetic aperture radar (SAR) images acquired from different viewing angles into homogeneous height maps. The change detection is then carried out in these height maps, thus mitigating any former geometric or radiometric differences of the imagery. An experiment with Capella data observing the city of Mariupol during the Russian war against Ukraine illustrates both the potential and possible risks of the approach.

Height estimation from single images has become a highly requested topic in the remote sensing community. While most methods use optical data, a first attempt with SAR intensity imagery was recently performed and achieved promising results. The actual practical value of any Deep Learning-based method such as this, however, then depends on how well it can be applied to sceneries unknown to the model, maybe even recorded under different acquisition conditions. This paper focuses on that very aspect of this methodology. For this purpose, the differences of distinct data types and test scenes are highlighted and the obtained results by the pre-trained models are evaluated and interpreted.