Automatic Feature-Based Geometric Fusion of Multiview TomoSAR Point Clouds in Urban Area

Interferometric synthetic aperture radar (InSAR) techniques, such as persistent scatterer interferometry (PSI) or SAR tomography (TomoSAR), deliver three-dimensional (3-D) point clouds of the scatterers' positions together with their motion information relative to a reference point. Due to the SAR side-looking geometry, minimum of two point clouds from cross-heading orbits, i.e., ascending and descending, are required to achieve a complete monitoring over an urban area. However, these two point clouds are usually not coregistered due to their different reference points with unknown 3-D positions. In general, no exact identical points from the same physical object can be found in such two point clouds. This article describes a robust algorithm for fusing such two point clouds of urban areas. The contribution of this paper is finding the theoretically exact point correspondence, which is the end positions of façades, where the two point clouds close. We explicitly define this algorithm as “L-shape detection and matching,” in this paper, because the façades commonly appear as L-shapes in InSAR point cloud. This algorithm introduces a few important features for a reliable result, including point density estimation using adaptive directional window for better façade points detection and L-shape extraction using weighed Hough transform. The algorithm is fully automatic. Its accuracy is evaluated using simulated data. Furthermore, the proposed method is applied on two TomoSAR point clouds over Berlin with ascending and descending geometry. The result is compared with the first PSI point cloud fusion method (S. Gernhardt and R. Bamler, “Deformation monitoring of single buildings using meter-resolution SAR data in PSI,” ISPRS J. Photogramm. Remote Sens., vol. 73, pp. 68-79, 2012.) for urban area. Submeter consistency is achieved.