面向匹配优化的多源建筑物实体级保形空间对齐模型

doi:10.11947/j.AGCS.2025.20250162

测绘学报 ›› 2025, Vol. 54 ›› Issue (12): 2262-2275.doi: 10.11947/j.AGCS.2025.20250162

面向匹配优化的多源建筑物实体级保形空间对齐模型

邱越(), 武芳(), 翟仁健, 钱海忠, 黄哲琨, 李博

信息工程大学地理空间信息学院，河南　郑州　450001

收稿日期:2025-04-16 修回日期:2025-11-01 出版日期:2026-01-15 发布日期:2026-01-15
通讯作者: 武芳 E-mail:qiuyue@whu.edu.cn;wufang_630@126.com
作者简介:邱越（1997—），男，博士生，研究方向为地理空间数据智能处理。　E-mail：qiuyue@whu.edu.cn
基金资助:
国家自然科学基金(42201491; 42271463; 42301521)

An entity-level conformal spatial alignment model for multi-source building matching optimization

Yue QIU(), Fang WU(), Renjian ZHAI, Haizhong QIAN, Zhekun HUANG, Bo LI

Institute of Geospatial Information, University of Information Engineering, Zhengzhou 450001, China

Received:2025-04-16 Revised:2025-11-01 Online:2026-01-15 Published:2026-01-15
Contact: Fang WU E-mail:qiuyue@whu.edu.cn;wufang_630@126.com
About author:QIU Yue (1997—), male, PhD candidate, majors in intelligent geospatial data processing. E-mail: qiuyue@whu.edu.cn
Supported by:
The National Natural Science Foundation of China(42201491; 42271463; 42301521)

摘要/Abstract

摘要：

多源建筑物矢量数据的精确匹配与融合对城市空间分析与应用至关重要，然而其普遍存在的空间位置不一致性构成了主要技术瓶颈，严重制约匹配精度与数据融合质量。现有空间对齐方法或陷入“先匹配后对齐”的循环依赖困境，无法从根本上改善匹配条件；或采用全局/局部变换模型，难以精细校正实体级非线性偏差，且常因引入几何形变而干扰后续基于形态的匹配判据。针对上述局限，本文提出一种面向匹配优化的多源建筑物实体级保形空间对齐模型，采用“矢量-栅格-矢量”协同工作流，在匹配流程前独立执行：首先，提取建筑物质心构建Delaunay三角网以表征空间结构，并将其栅格化；其次，在栅格域运用全局-局部渐进式特征匹配策略，高效识别高置信度同名点对；然后，基于可靠同名点构建连续精细的位移场，且关键在于，利用该位移场驱动每个源建筑物多边形进行整体刚性平移，在精确校正位置偏差的同时严格保持其固有几何形状；最后，结合拓扑冲突检测与消解机制确保空间有效性。试验结果表明，本文方法显著改善了多源数据的空间一致性，平均Hausdorff距离相对减小18.23%，并使多种下游匹配算法的F₁值分别获得1.09至6.65个百分点不等的绝对提升量。试验证实了本文方法作为一种高效的预处理策略，在提升多源建筑物矢量数据匹配精度与融合质量方面的有效性与应用潜力。

关键词: 建筑物匹配, 空间对齐, 矢量数据, 位移场, 矢量-栅格-矢量, 空间校正

Abstract:

Accurate matching and integration of multi-source building vector data are crucial for urban spatial analysis and applications. However, the prevalent spatial inconsistency significantly hinders matching accuracy and data fusion quality. Existing spatial alignment methods either adopt a “matching-then-alignment” pipeline that suffers from a circular dependency between alignment quality and matching accuracy and thus fails to fundamentally improve matching conditions, or utilize global/local transformation models that struggle to accurately correct entity-level nonlinear distortions and often introduce geometric deformations that interfere with subsequent morphology-based matching criteria. To address these limitations, this paper proposes a novel entity-level conformal spatial alignment model for multi-source building matching optimization, employing an innovative “vector-raster-vector” collaborative workflow. This approach independently executes prior to the matching process: first, it extracts building centroids to construct a Delaunay triangulation representing spatial structures and rasterizes it; second, it applies a global-local progressive feature matching strategy in the raster domain to efficiently identify high-confidence corresponding points; subsequently, it constructs a continuous displacement field based on reliable correspondences. Crucially, this displacement field drives each source building polygon to undergo an overall rigid translation, accurately correcting positional deviations while strictly preserving their inherent geometric shapes, ultimately ensuring spatial validity through topological conflict detection and resolution mechanisms. Experimental results demonstrate that this method significantly enhances spatial consistency among multi-source data, with an average Hausdorff distance reduction of 18.23% and substantial improvements in F₁ scores ranging from 1.09 to 6.65 percentage points across various downstream matching algorithms. The experiments confirm the effectiveness and application potential of this method as a preprocessing strategy for enhancing the accuracy and quality of multi-source building vector data matching and integration.

Key words: building matching, spatial alignment, vector data, displacement field, vector-raster-vector, spatial correction

中图分类号:

P208

邱越, 武芳, 翟仁健, 钱海忠, 黄哲琨, 李博. 面向匹配优化的多源建筑物实体级保形空间对齐模型[J]. 测绘学报, 2025, 54(12): 2262-2275.

Yue QIU, Fang WU, Renjian ZHAI, Haizhong QIAN, Zhekun HUANG, Bo LI. An entity-level conformal spatial alignment model for multi-source building matching optimization[J]. Acta Geodaetica et Cartographica Sinica, 2025, 54(12): 2262-2275.

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指标	定义	参数	说明
互信息（MI）	MI（A，B）=H（A）+H（B）－H（A，B）	H（•）为信息熵，A、B为标准化后的栅格图像	度量信息共享程度，值越大表明分布协同性越强
均方误差（MSE）		M、N为图像尺寸	直接反映像素级差异，值越小表明对齐精度越高
峰值信噪比（PSNR）		MAX为图像最大值	对数尺度敏感度指标，值越大表明对齐质量越高
结构相似性指数（SSIM）		μ为均值，σ为方差，C₁、C₂均为稳定性常数	综合亮度、对比度、结构相似性，越接近1越好
归一化互相关（NCC）		μ_A、μ_B均为图像均值	衡量线性相关性，值越接近1表明分布模式越相似
Dice系数		为像素集合的基数	量化空间重叠度，值越接近1表明重叠区域越大

指标	参数	说明
查准率（Precision）	TP：真正例 FP：假正例	度量匹配准确性，值越大误匹配越少
查全率（Recall）	FN：假负例	度量匹配完整性，值越大漏匹配越少
F₁值（F₁score）	Precision：查准率 Recall：查全率	综合准确性与完整性，值越大匹配性能越优

指标	对齐前	对齐后	相对提升量/（%）	绝对提升量
MSE	0.149 9	0.116 7	22.15	0.033 2
NCC	0.545 9	0.647 6	18.63	0.101 7
PSNR	8.240 9	9.327 9	13.19	1.087 0
MI	0.184 6	0.261 3	41.55	0.076 7
SSIM	0.779 3	0.816 0	4.71	0.036 7
DICE	0.640 5	0.721 2	12.60	0.080 7
Hausdorff	100.121 4	88.763 0	11.34	11.358 4
平均Hausdorff	8.660 2	7.081 6	18.23	1.578 6

指标	对齐前	本文方法		人工标记1对		人工标记5对		人工标记9对
指标	对齐前	对齐后	相对提升量/（%）	对齐后	相对提升量/（%）	对齐后	相对提升量/（%）	对齐后	相对提升量/（%）
MSE	0.149 9	0.116 7	22.15	0.151 0	－0.73	0.141 3	5.74	0.136 4	9.01
NCC	0.545 9	0.647 6	18.63	0.542 9	－0.55	0.574 0	5.15	0.588 4	7.79
PSNR	8.240 9	9.327 9	13.19	8.210 8	－0.37	8.498 9	3.13	8.652 0	4.99
MI	0.184 6	0.261 3	41.55	0.182 5	－1.14	0.204 6	10.83	0.214 9	16.41
SSIM	0.779 3	0.816 0	4.71	0.778 4	－0.12	0.788 8	1.22	0.793 8	1.86
DICE	0.640 5	0.721 2	12.60	0.638 1	－0.37	0.663 3	3.56	0.674 5	5.31
Hausdorff	100.121 4	88.763 0	11.34	88.206 1	11.90	87.080 3	13.03	87.080 3	13.03
平均Hausdorff	8.660 2	7.081 6	18.23	8.415 0	2.83	8.505 4	1.79	7.990 5	7.73

匹配方法	对齐前			对齐后			绝对提升量
匹配方法	查准率	查全率	F₁值	查准率	查全率	F₁值	查准率	查全率	F₁值
位置法	60.29	76.46	67.42	66.72	81.93	73.55	6.43	5.47	6.13
重叠法	71.74	83.39	77.13	79.83	88.14	83.78	8.09	4.75	6.65
3指标法	77.44	89.60	83.08	79.23	89.78	84.17	1.79	0.18	1.09
6指标法	64.74	79.74	71.46	71.60	85.58	77.97	6.86	5.84	6.51

面向匹配优化的多源建筑物实体级保形空间对齐模型

An entity-level conformal spatial alignment model for multi-source building matching optimization

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匹配方法	查准率绝对提升量				查全率绝对提升量				F₁值绝对提升量
匹配方法	人工-1	人工-5	人工-9	本文方法	人工-1	人工-5	人工-9	本文方法	人工-1	人工-5	人工-9	本文方法
位置法	－8.02	－2.69	2.37	6.43	－7.12	－1.10	1.64	5.47	－7.81	－2.12	2.12	6.13
重叠法	－7.93	2.11	2.78	8.09	－4.56	4.20	2.01	4.75	－6.60	3.00	2.46	6.65
3指标法	－0.22	0.50	0.30	1.79	－2.37	－1.28	－1.64	0.18	－1.16	－0.27	－0.55	1.09
6指标法	－6.72	4.39	3.80	6.86	－5.83	4.02	2.56	5.84	－6.45	4.28	3.33	6.51

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