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Article

Automatic Regularization of TomoSAR Point Clouds for Buildings Using Neural Networks

by 1,2, 1,2, 2,*, 1,2 and 1,2
1
School of Electronics, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China
2
National Key Lab of Microwave Imaging Technology, Institute of Electronics, Chinese Academy of Sciences, Beijing 100190, China
*
Author to whom correspondence should be addressed.
Sensors 2019, 19(17), 3748; https://doi.org/10.3390/s19173748
Received: 26 June 2019 / Revised: 19 August 2019 / Accepted: 25 August 2019 / Published: 30 August 2019
(This article belongs to the Section Remote Sensors)
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. View Full-Text
Keywords: denoising; neural networks; regularization; 3-D point clouds; tomographic SAR denoising; neural networks; regularization; 3-D point clouds; tomographic SAR
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MDPI and ACS Style

Zhou, S.; Li, Y.; Zhang, F.; Chen, L.; Bu, X. Automatic Regularization of TomoSAR Point Clouds for Buildings Using Neural Networks. Sensors 2019, 19, 3748. https://doi.org/10.3390/s19173748

AMA Style

Zhou S, Li Y, Zhang F, Chen L, Bu X. Automatic Regularization of TomoSAR Point Clouds for Buildings Using Neural Networks. Sensors. 2019; 19(17):3748. https://doi.org/10.3390/s19173748

Chicago/Turabian Style

Zhou, Siyan, Yanlei Li, Fubo Zhang, Longyong Chen, and Xiangxi Bu. 2019. "Automatic Regularization of TomoSAR Point Clouds for Buildings Using Neural Networks" Sensors 19, no. 17: 3748. https://doi.org/10.3390/s19173748

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