Towards a Semantic Interpretation of Urban Areas With Airborne Synthetic Aperture Radar Tomography

D'Hondt, O.; Guillaso, Stéphane; Hellwich, Olaf

doi:10.5194/isprsannals-iii-7-235-2016

Cited by 6 publications

(1 citation statement)

References 15 publications

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“…The footprints are regularized according to some particular shapes, for example, the L-shape. On this basis, many other methods of building modeling have been proposed [20,21,22,23,24,25,26]. They mainly used similar projection method firstly and reconstructed the 2-D building model on the ground plane.…”

Section: Introductionmentioning

confidence: 99%

Automatic Regularization of TomoSAR Point Clouds for Buildings Using Neural Networks

Zhou

Zhang

et al. 2019

Sensors

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Tomographic SAR (TomoSAR) is a remote sensing technique that extends the conventional two-dimensional (2-D) synthetic aperture radar (SAR) imaging principle to three-dimensional (3-D) imaging. It produces 3-D point clouds with unavoidable noise that seriously deteriorates the quality of 3-D imaging and the reconstruction of buildings over urban areas. However, existing methods for TomoSAR point cloud processing notably rely on data segmentation, which influences the processing efficiency and denoising performance to a large extent. Inspired by regression analysis, in this paper, we propose an automatic method using neural networks to regularize the 3-D building structures from TomoSAR point clouds. By changing the point heights, the surface points of a building are refined. The method has commendable performance on smoothening the building surface, and keeps a precise preservation of the building structure. Due to the regression mechanism, the method works in a high automation level, which avoids data segmentation and complex parameter adjustment. The experimental results demonstrate the effectiveness of our method to denoise and regularize TomoSAR point clouds for urban buildings.

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