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


Interferometric synthetic aperture radar (InSAR) techniques, like persistent scatterer interferometry (PSI) or SAR tomography (TomoSAR), deliver 3-dimensional (3-d) point clouds of the scatterers' positions along with their motion info relative to a reference purpose. Due to the SAR side-looking geometry, minimum of two point clouds from cross-heading orbits, i.e., ascending and descending, are needed to attain an entire monitoring over an urban area. However, these two purpose clouds are typically not coregistered because of their totally different reference points with unknown three-D positions. In general, no actual identical points from the identical physical object will be found in such 2 point clouds. This article describes a strong algorithm for fusing such two purpose clouds of urban areas. The contribution of this paper is finding the theoretically actual purpose correspondence, that is the end positions of façades, where the two point clouds shut. We have a tendency to explicitly outline this algorithm as “L-shape detection and matching,” in this paper, as a result of the façades commonly seem as L-shapes in InSAR point cloud. This algorithm introduces a few important options for a reliable result, as well as purpose density estimation using adaptive directional window for better façade points detection and L-shape extraction using weighed Hough rework. The algorithm is fully automatic. Its accuracy is evaluated using simulated data. Furthermore, the proposed method is applied on 2 TomoSAR point clouds over Berlin with ascending and descending geometry. The result's compared with the primary PSI purpose cloud fusion methodology (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.

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