Unsupervised Motion Layer Segmentation by Random Sampling and Energy Minimization

D'Hondt, O.; Caselles, Vicent

doi:10.1109/cvmp.2010.25

Cited by 2 publications

(1 citation statement)

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“…Similarly, based on the RANSAC algorithm, the TomoSeed method [14] led this plane fitting method to process TomoSAR point clouds. The region growing [15,16] was also an inspiration to this method. TomoSeed first selects seeds and retrieves planar patches according to the spatial connectivity of the scatterers.…”

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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Section: Introductionmentioning

confidence: 99%