Zhou Siyan, Li Yanlei, Zhang Fubo, Chen Longyong, Bu Xiangxi
School of Electronics, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China.
National Key Lab of Microwave Imaging Technology, Institute of Electronics, Chinese Academy of Sciences, Beijing 100190, China.
Sensors (Basel). 2019 Aug 30;19(17):3748. doi: 10.3390/s19173748.
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.
层析合成孔径雷达（TomoSAR）是一种遥感技术，它将传统的二维（2-D）合成孔径雷达（SAR）成像原理扩展到三维（3-D）成像。它生成的三维点云带有不可避免的噪声，这严重降低了三维成像的质量以及城市区域建筑物的重建效果。然而，现有的TomoSAR点云处理方法显著依赖于数据分割，这在很大程度上影响了处理效率和去噪性能。受回归分析的启发，本文提出了一种使用神经网络从TomoSAR点云对三维建筑结构进行正则化的自动方法。通过改变点的高度，对建筑物的表面点进行细化。该方法在平滑建筑物表面方面具有值得称赞的性能，并能精确保留建筑物结构。由于回归机制，该方法自动化程度高，避免了数据分割和复杂的参数调整。实验结果证明了我们的方法对城市建筑物的TomoSAR点云进行去噪和正则化的有效性。
