  Building height retrieval from synthetic aperture radar (SAR) imagery is of
great importance for urban applications, yet highly challenging owing to the
complexity of SAR data. This paper addresses the issue of building height
retrieval in large-scale urban areas from a single TerraSAR-X spotlight or
stripmap image. Based on the radar viewing geometry, we propose that this
problem can be formulated as a bounding box regression problem and therefore
allows for integrating height data from multiple data sources in generating
ground truth on a larger scale. We introduce building footprints from
geographic information system (GIS) data as complementary information and
propose a bounding box regression network that exploits the location
relationship between a building's footprint and its bounding box, allowing for
fast computation. This is important for large-scale applications. The method is
validated on four urban data sets using TerraSAR-X images in both
high-resolution spotlight and stripmap modes. Experimental results show that
the proposed network can reduce the computation cost significantly while
keeping the height accuracy of individual buildings compared to a Faster R-CNN
based method. Moreover, we investigate the impact of inaccurate GIS data on our
proposed network, and this study shows that the bounding box regression network
is robust against positioning errors in GIS data. The proposed method has great
potential to be applied to regional or even global scales.
