Automatic Registration of Very Low Overlapping Array InSAR Point Clouds in Urban Scenes

Tong, Xiaohua; Zhang, Xin; Liu, Shijie; Ye, Zhen; Feng, Yongjiu; Xie, Huan; Chen, Longyong; Zhang, Fubo; Han, Jie; Jin, Yanmin; Chen, Hao

doi:10.1109/tgrs.2022.3153185

Cited by 5 publications

(24 citation statements)

References 58 publications

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“…Due to the low overlap between the SAR point clouds obtained from two flight experiments, these metrics cannot directly evaluate the effectiveness of SAR point cloud registration. Therefore, we adopt the metrics proposed by [31]. Ref.…”

Section: Evaluation Criterionmentioning

confidence: 99%

“…Ref. [31] utilizes the constraint of parallel relative facades of the same building to extract the building facades from the fused point cloud. For effective registration methods, the directions of the two relative facades should be parallel.…”

Section: Evaluation Criterionmentioning

confidence: 99%

“…Hence, as illustrated in Figure 11, ref. [31] calculate the angular difference θ between the two normal vectors of each facade pair. The evaluation metrics proposed in [31] only capture the effectiveness of building point registration.…”

Section: Evaluation Criterionmentioning

confidence: 99%

“…Dr. Zhu proposed an approach to extract the L-shaped structures of buildings in tomographic SAR point clouds and achieve automatic registration of point clouds from different scans [30]. Dr. Tong from Tongji University proposed a method that utilizes the constraint of parallel building facades to match specific pairs of building facades [31]. However, the bottom scenes of buildings have holes due to occlusion, and there is a large amount of noise below the building facades due to third-order scattering [32].…”

Section: Introductionmentioning

confidence: 99%

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A Novel Automatic Registration Method for Array InSAR Point Clouds in Urban Scenes

Cui,

Liu,

Zhang

et al. 2024

Remote Sensing

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The array interferometric synthetic aperture radar (Array InSAR) system resolves shadow issues by employing two scans in opposite directions, facilitating the acquisition of a comprehensive three-dimensional representation of the observed scene. The point clouds obtained from the two scans need to be transformed into the same coordinate system using registration techniques to create a more comprehensive visual representation. However, the two-point clouds lack corresponding points and exhibit distinct geometric distortions, thereby preventing direct registration. This paper analyzes the error characteristics of array InSAR point clouds and proposes a robust registration method for array InSAR point clouds in urban scenes. It represents the 3D information of the point clouds using images, with pixel positions corresponding to the azimuth and ground range directions. Pixel intensity denotes the average height of points within the pixel. The KAZE algorithm and enhanced matching approach are used to obtain the homonymous points of two images, subsequently determining the transformation relationship between them. Experimental results with actual data demonstrate that, for architectural elements within urban scenes, the relative angular differences of registered facades are below 0.5°. As for ground elements, the Root Mean Square Error (RMSE) after registration is less than 1.5 m, thus validating the superiority of the proposed method.

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Section: Evaluation Criterionmentioning

confidence: 99%

Section: Evaluation Criterionmentioning

confidence: 99%

Section: Evaluation Criterionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Novel Automatic Registration Method for Array InSAR Point Clouds in Urban Scenes

Cui,

Liu,

Zhang

et al. 2024

Remote Sensing

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“…Hence, this method was limited to buildings with L-shaped facades. A robust alignment method for urban area array InSAR point clouds was proposed in [ 29 ], using the concave and convex facades of buildings for rotation correction and fine displacement. The TomoSAR and MLS point clouds in urban areas also have rich facade information.…”

Section: Introductionmentioning

confidence: 99%

Automatic Registration of Homogeneous and Cross-Source TomoSAR Point Clouds in Urban Areas

Pang

Liu

et al. 2023

Sensors

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Building reconstruction using high-resolution satellite-based synthetic SAR tomography (TomoSAR) is of great importance in urban planning and city modeling applications. However, since the imaging mode of SAR is side-by-side, the TomoSAR point cloud of a single orbit cannot achieve a complete observation of buildings. It is difficult for existing methods to extract the same features, as well as to use the overlap rate to achieve the alignment of the homologous TomoSAR point cloud and the cross-source TomoSAR point cloud. Therefore, this paper proposes a robust alignment method for TomoSAR point clouds in urban areas. First, noise points and outlier points are filtered by statistical filtering, and density of projection point (DoPP)-based projection is used to extract TomoSAR building point clouds and obtain the facade points for subsequent calculations based on density clustering. Subsequently, coarse alignment of source and target point clouds was performed using principal component analysis (PCA). Lastly, the rotation and translation coefficients were calculated using the angle of the normal vector of the opposite facade of the building and the distance of the outer end of the facade projection. The experimental results verify the feasibility and robustness of the proposed method. For the homologous TomoSAR point cloud, the experimental results show that the average rotation error of the proposed method was less than 0.1°, and the average translation error was less than 0.25 m. The alignment accuracy of the cross-source TomoSAR point cloud was evaluated for the defined angle and distance, whose values were less than 0.2° and 0.25 m.

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SphereNet: Learning a Noise-Robust and General Descriptor for Point Cloud Registration

Zhao,

Guo,

Wang

et al. 2024

IEEE Trans. Geosci. Remote Sensing

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Automatic Registration of Very Low Overlapping Array InSAR Point Clouds in Urban Scenes

Cited by 5 publications

References 58 publications

A Novel Automatic Registration Method for Array InSAR Point Clouds in Urban Scenes

A Novel Automatic Registration Method for Array InSAR Point Clouds in Urban Scenes

Automatic Registration of Homogeneous and Cross-Source TomoSAR Point Clouds in Urban Areas

SphereNet: Learning a Noise-Robust and General Descriptor for Point Cloud Registration

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