Model based building recognition from multi-aspect InSAR data in urban areas

The achievable ground resolution of state-of-the-art synthetic aperture radar (SAR) sensors enables the analysis of urban areas with industrial as well as residential character. In this paper, an approach is proposed to detect and reconstruct small as well as extended buildings from multi-aspect high resolution InSAR data sets. The recognition of buildings is supported by knowledge based analysis considering SAR-specific effects such as layover, radar shadow and multipath signal propagation. But especially in dense built up areas those effects can also lead to a reduction of the reconstruction quality e.g. in the case of adjacent trees or other buildings. In those cases the results can be significantly improved by a combined analysis of multi-aspect data. The presented approach exploits amplitude, phase, coherence data and classification results. That is demonstrated in an urban environment for an InSAR data set, which has a spatial resolution of about 30 cm and was taken from two orthogonal flight directions.