Paper Info
Title
Automatic Regularization of TomoSAR Point Clouds for Buildings Using Neural Networks.
Abstract
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.
Year
DOI
Venue
2019
10.3390/s19173748
SENSORS
Keywords
Field
DocType
denoising,neural networks,regularization,3-D point clouds,tomographic SAR
Noise reduction,Computer vision,Data segment,Point cloud processing,Synthetic aperture radar,Automation,Electronic engineering,Regularization (mathematics),Artificial intelligence,Engineering,Artificial neural network,Point cloud
Journal
Volume
Issue
ISSN
19
17
1424-8220
Citations 
PageRank 
References 
0
0.34
0
Authors
5
Name
Order
Citations
PageRank
Siyan Zhou100.34
Yan-lei Li255.54
Fubo Zhang3447.79
Longyong Chen4457.11
Xiangxi Bu500.34