面向阵列InSAR点云规则化的渐近式建筑立面检测

王伟; 许华荣; 魏含玉; 董秋雷

doi:10.12000/JR21177

面向阵列InSAR点云规则化的渐近式建筑立面检测

DOI: 10.12000/JR21177

王伟¹,
许华荣²,
魏含玉³,
董秋雷^{4, 5, ,}

1.
周口师范学院网络工程学院周口 466001
2.
厦门理工学院计算机与信息工程学院厦门 361024
3.
周口师范学院数学与统计学院周口 466001
4.
中国科学院自动化研究所北京 100190
5.
中国科学院大学人工智能学院北京 100049

基金项目: 国家自然科学基金(61991423, U1805264)，空间光电测量与感知实验室开放基金(502K0019118)，河南省科技攻关项目(212102310397)

详细信息

作者简介:
王　伟(1976–)，男，河南人，博士，周口师范学院网络工程学院副教授。主要研究方向为计算机视觉

许华荣(1970–)，男，福建人，博士，厦门理工学院计算机与信息工程学院教授。主要研究方向为计算机视觉

魏含玉(1982–)，男，河南人，博士，周口师范学院数学与统计学院副教授。主要研究方向为孤立子与可积系统

董秋雷(1980–)，男，辽宁人，博士，中国科学院自动化研究所研究员，中国科学院大学人工智能学院岗位教授。主要研究方向为计算机视觉与模式分类

通讯作者:
董秋雷 qldong@nlpr.ia.ac.cn

责任主编：胡俊 Corresponding Editor: HU Jun
中图分类号: TN957.52
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出版历程
- 收稿日期: 2021-11-11
- 修回日期: 2022-01-28
- 网络出版日期: 2022-02-25
- 刊出日期: 2022-02-28

Progressive Building Facade Detection for Regularizing Array InSAR Point Clouds

WANG Wei¹,
XU Huarong²,
WEI Hanyu³,
DONG Qiulei^{4, 5
, ,}

1.
School of Network Engineering, Zhoukou Normal University, Zhoukou 466001, China
2.
School of Computer Information Engineering, Xiamen University of Technology, Xiamen 361024, China
3.
College of Mathematics and Statistics, Zhoukou Normal University, Zhoukou 466001, China
4.
Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
5.
School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China

Funds: The National Natural Science Foundation of China (61991423, U1805264), The Foundation of the Lab of Space Optoelectronic Measurement & Perception (502K0019118), The Science and Technique Project of Henan (212102310397)

More Information

Corresponding author: DONG Qiulei, qldong@nlpr.ia.ac.cn

摘要

摘要: 为有效地从海量、带噪阵列InSAR空间点中检测建筑立面，该文提出一种基于结构先验的渐进式建筑立面检测算法。该文算法首先将初始阵列InSAR空间点投影至地面以生成与建筑立面相应的连通区域，然后通过结构先验的引导逐步在每个连通区域内检测潜在的线段，进而根据线段及其对应的空间点生成相应的建筑立面；在此过程中，当前连通区域对应线段的检测空间根据其相邻连通区域内已检测线段构造，有效保证了整体效率与可靠性。实验结果表明，该文算法可快速从海量、带噪阵列InSAR空间点中检测出较多的可靠建筑立面，较好地克服了传统多模型拟合算法效率低与可靠性差的缺点。
- 阵列InSAR点云 /
- 结构先验 /
- 平面拟合 /
- 三维建模
Abstract: This study proposes a progressive building facade detection method based on structure priors to effectively detect building facades from massive array InSAR spatial points with noise. First, the proposed method projects the initial array of InSAR three-Dimensional (3D) points on the ground to produce connected regions that correspond to building facades and then progressively detects potential line segments in each connected region under the guidance of structure priors. Furthermore, the proposed method generates building facades according to the detected line segments and their corresponding 3D points. In this process, the line segment detection space of the current connected region is constructed based on line segments detected in its neighboring connected regions, thereby improving the overall efficiency and reliability of the current line segment detection. Experimental results confirm that the proposed method can efficiently produce more reliable building facades from a massive array of InSAR 3D points with noise, overcoming several difficulties (such as low efficiency and inferior reliability) encountered in traditional multi-model fitting methods.
- Array InSAR point cloud /
- Structure prior /
- Plane fitting /
- 3D modeling

HTML全文

图 1 PBFD算法流程(黄: 投影图生成，蓝: 主线段检测，绿: 潜在线段检测)

Figure 1. Flowchart of the PBFD method (yellow: projection map generation, blue: main line segment detection, green: potential line segment detection)

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图 2 投影图生成

Figure 2. Projection map generation

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图 3 主线段检测

Figure 3. Main line segment detection

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图 4 连通区域内潜在线段检测

Figure 4. Potential line segment detection in connected regions

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图 5 连通区域间潜在线段检测

Figure 5. Potential line segment detection between connected regions

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图 6 不同算法检测的直线

Figure 6. Lines produced by different methods

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图 7 平面生成示例

Figure 7. Illustration of plane generation

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图 8 最终生成的建筑立面

Figure 8. Final generated facades

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图 9 光学图像示例

Figure 9. Examples of optical images

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图 10 不同权重对应的精度

Figure 10. Accuracies corresponding to different weights

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图 11 建筑立面检测

Figure 11. Building facade detection

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图 12 不同算法检测的直线

Figure 12. Lines produced by different methods

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算法1：连通区域间潜在线段检测
步骤1　从集合$\bar{\mathcal{M} }$中选择具有最高优先级的连通区域$ \mathit{c} $。
步骤2　根据集合$ \mathit{N}\left(\mathit{c}\right) $内连通区域相应线段与结构先验生成候选直线。
步骤3　根据式(4)为连通区域$ \mathit{c} $分配最优直线。
步骤4　将连通区域$ \mathit{c} $移入集合$ \mathcal{M} $并转至步骤1。
步骤5　若集合$\bar{\mathcal{M} }$为空则输出集合$ \mathcal{M} $中更新的连通区域及相应的线段。

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表 1 数据基本信息

Table 1. Basic data information

数据集	空间点	初始地面点	真实直线数
峨眉	1338 K	2955	55
运城	1693 K	3193	40

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表 2 不同算法的精度

Table 2. Accuracies of different methods

数据集	PBFD												CONSAC
	Stage-I				Stage-II				Stage-III				CONSAC
	N-G-L	P	R	F1	N-G-L	P	R	F1	N-G-L	P	R	F1	P	R	F1
峨眉	33-5-12	1	0.22	0.36	83-7-43	0.94	0.73	0.82	109-7-57	0.90	0.93	0.91	0.79	0.24	0.37
运城	27-1-7	1	0.18	0.31	71-2-25	0.97	0.60	0.74	86-2-34	0.94	0.80	0.86	0.86	0.15	0.26

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表 3 不同算法运行时间(秒)

Table 3. Running time of different methods (s)

数据集	PBFD						CONSAC
数据集	INI	Stage-I	Stage-II	Stage-III	LP	Total	CONSAC
峨眉	2.1	2.4	3.1	2.5	1.7	11.8	7.3
运城	1.8	3.1	4.3	2.0	1.5	12.7	9.5

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