融合全局和局部特征的建筑物形状智能分类方法

doi:10.11947/j.AGCS.2024.20240040

测绘学报 ›› 2024, Vol. 53 ›› Issue (9): 1842-1852.doi: 10.11947/j.AGCS.2024.20240040

• 地图学与地理信息 • 上一篇

融合全局和局部特征的建筑物形状智能分类方法

张付兵¹(), 孙群¹(), 马京振¹^,²^,³, 孙士杰¹, 温伯威¹^,⁴^,⁵

^1.信息工程大学地理空间信息学院，河南　郑州　450052
^2.61540部队，陕西　西安　710054
^3.智慧地球重点实验室，北京　100029
^4.智慧中原地理信息技术河南省协同创新中心，河南　郑州　450052
^5.时空感知与智能处理自然资源部重点实验室，河南　郑州　450052

收稿日期:2024-01-22 发布日期:2024-10-16
通讯作者: 孙群 E-mail:zhangfbing@163.com;13503712102@163.com
作者简介:张付兵（1997—），男，博士生，主要从事数字地图制图与制图综合研究。E-mail：zhangfbing@163.com
基金资助:
国家自然科学基金(42101454);智慧地球重点实验室基金(KF2023YB02-02)

An intelligent classification method for building shape based on fusion of global and local features

Fubing ZHANG¹(), Qun SUN¹(), Jingzhen MA¹^,²^,³, Shijie SUN¹, Bowei WEN¹^,⁴^,⁵

^1.Institute of Geospatial Information, University of Information Engineering, Zhengzhou 450052, China
^2.Troops 61540, Xi'an 710054, China
^3.Key Laboratory of Smart Earth, Beijing 100029, China
^4.Collaborative Innovation Center of Geo-information Technology for Smart Central Plains, Zhengzhou 450052, China
^5.Key Laboratory of Spatiotemporal Perception and Intelligent processing, Ministry of Natural Resources, Zhengzhou 450052, China

Received:2024-01-22 Published:2024-10-16
Contact: Qun SUN E-mail:zhangfbing@163.com;13503712102@163.com
About author:ZHANG Fubing (1997—), male, PhD candidate, majors in digital cartography and cartographic generalization. E-mail: zhangfbing@163.com
Supported by:
The National Natural Science Foundation of China(42101454);Key Laboratory of Smart Earth of China(KF2023YB02-02)

摘要/Abstract

摘要：

深度学习方法支持下的建筑物形状认知成为地图制图等领域研究的热点，利用深度学习的特征挖掘能力，可以提取形状的嵌入表示，支撑制图综合、空间查询等应用场景。本文以建筑物数据为例，构建了一种融合全局特征和图节点特征的建筑物形状分类的图谱卷积神经网络模型。首先，在建筑物加权图基础上分别以建筑物4个宏观形状特征、边界顶点的多阶局部和区域结构特征生成形状的融合描述；然后，利用图谱卷积神经网络提取多层次形状信息，通过融合不同层的图表示结果生成特征编码用于形状分类。试验结果表明，相较对比方法，本文方法能够更有效地区分不同建筑物的形状类别，且生成的特征编码具有良好的形状区分度。

关键词: 形状认知, 图卷积神经网络, 建筑物形状分类, 特征融合, 图分类

Abstract:

Supported by deep learning methods for building shape cognition, it has become a hot research topic in fields such as cartography. The feature mining ability of deep learning can help extract embedded representations of shapes, supporting application scenarios such as cartographic generalization and spatial retrieval. A graph convolutional neural network model for building shape classification that integrates global features and graph node features is constructed, and validated using building data as an example. Firstly, a weighted building graph is constructed, and then a fusion description of the shape is generated based on the 4 macroscopic shape features of building and the multi-level local and regional structural features of boundary vertice. Graph convolutional neural networks are used to extract multi-level shape information, and the feature coding generated by fusing graph representations from different layers is used for shape classification.The experimental results show that compared to the comparative method, the proposed method is more effective in distinguishing the shape categories of different buildings, and the generated feature coding have positive shape discrimination.

Key words: shape cognition, graph convolutional neural network, building shape classification, feature fusion, graph classification

中图分类号:

P208

张付兵, 孙群, 马京振, 孙士杰, 温伯威. 融合全局和局部特征的建筑物形状智能分类方法[J]. 测绘学报, 2024, 53(9): 1842-1852.

Fubing ZHANG, Qun SUN, Jingzhen MA, Shijie SUN, Bowei WEN. An intelligent classification method for building shape based on fusion of global and local features[J]. Acta Geodaetica et Cartographica Sinica, 2024, 53(9): 1842-1852.

图/表 15

图1

图2

图3

图4

表1

图5

图6

图7

图8

表2

表3

表4

表5

图9

表6

参考文献 31

[1]	晏雄锋, 艾廷华, 杨敏, 等. 地图空间形状认知的自编码器深度学习方法[J]. 测绘学报, 2021, 50(6):757-765. DOI: 10.11947/j.AGCS.2021.20210046.
	YAN Xiongfeng, AI Tinghua, YANG Min, et al. Shape cognition in map space using deep auto-encoder learning[J]. Acta Geodaetica et Cartographica Sinica, 2021, 50(6):757-765. DOI: 10.11947/j.AGCS.2021.20210046.
[2]	艾廷华, 帅赟, 李精忠. 基于形状相似性识别的空间查询[J]. 测绘学报, 2009, 38(4):356-362.
	AI Tinghua, SHUAI Yun, LI Jingzhong. A spatial query based on shape similarity cognition[J]. Acta Geodaetica et Cartographica Sinica, 2009, 38(4):356-362.
[3]	YAN Xiongfeng, AI Tinghua, YANG Min, et al. Graph convolutional autoencoder model for the shape coding and cognition of buildings in maps[J]. International Journal of Geographical Information Science, 2021, 35(3):490-512.
[4]	王斌. 一种基于多级弦长函数的傅立叶形状描述子[J]. 计算机学报, 2010, 33(12):2387-2396.
	WANG Bin. A Fourier shape descriptor based on multi-level chord length function[J]. Chinese Journal of Computers, 2010, 33(12):2387-2396.
[5]	BRODERSEN L, ANDERSEN H H K, WEBER S. Applying eye-movement tracking for the study of map perception and map design[R]. Copenhagen: National Survey and Cadastre Publications, 2002.
[6]	晏雄锋, 艾廷华, 杨敏. 居民地要素化简的形状识别与模板匹配方法[J]. 测绘学报, 2016, 45(7):874-882. DOI: 10.11947/j.AGCS.2016.20150162.
	YAN Xiongfeng, AI Tinghua, YANG Min. A simplification of residential feature by the shape cognition and template matching method[J]. Acta Geodaetica et Cartographica Sinica, 2016, 45(7):874-882. DOI: 10.11947/j.AGCS.2016.20150162.
[7]	晏雄锋, 袁拓, 杨敏, 等. 建筑物形状特征分析表达与自适应化简方法[J]. 测绘学报, 2022, 51(2):269-278. DOI: 10.11947/j.AGCS.2022.20210302.
	YAN Xiongfeng, YUAN Tuo, YANG Min, et al. An adaptive building simplification approach based on shape analysis and representation[J]. Acta Geodaetica et Cartographica Sinica, 2022, 51(2):269-278. DOI: 10.11947/j.AGCS.2022.20210302.
[8]	YAN Xiongfeng, AI Tinghua, YANG Min, et al. A graph convolutional neural network for classification of building patterns using spatial vector data[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2019, 150:259-273.
[9]	LI Wenwen, GOODCHILD M F, CHURCH R. An efficient measure of compactness for two-dimensional shapes and its application in regionalization problems[J]. International Journal of Geographical Information Science, 2013, 27(6):1227-1250.
[10]	BASARANER M, CETINKAYA S. Performance of shape indices and classification schemes for characterising perceptual shape complexity of building footprints in GIS[J]. International Journal of Geographical Information Science, 2017, 31(10):1952-1977.
[11]	FREEMAN H. On the encoding of arbitrary geometric configurations[J]. IEEE Transactions on Electronic Computers, 1961, EC-10(2):260-268.
[12]	刘鹏程, 艾廷华, 胡晋山, 等. 基于原型模板形状匹配的建筑多边形化简[J]. 武汉大学学报(信息科学版), 2010, 35(11):1369-1372.
	LIU Pengcheng, AI Tinghua, HU Jinshan, et al. Building-polygon simplification based on shape matching of prototype template[J]. Geomatics and Information Science of Wuhan University, 2010, 35(11):1369-1372.
[13]	魏智威, 郭庆胜, 程璐, 等. 建筑物图形形状相似性计算的序列分析法[J]. 测绘学报, 2021, 50(12):1683-1693. DOI: 10.11947/j.AGCS.2021.20200227.
	WEI Zhiwei, GUO Qingsheng, CHENG Lu, et al. Shape similarity measurement based on DNA alignment for buildings with multiple orthogonal features[J]. Acta Geodaetica et Cartographica Sinica, 2021, 50(12):1683-1693. DOI: 10.11947/j.AGCS.2021.20200227.
[14]	ARKIN E M, CHEW L P, HUTTENLOCHER D P, et al. An efficiently computable metric for comparing polygonal shapes[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1991, 13(3):209-216.
[15]	AI Tinghua, CHENG Xiaoqiang, LIU Pengcheng, et al. A shape analysis and template matching of building features by the Fourier transform method[J]. Computers, Environment and Urban Systems, 2013, 41:219-233.
[16]	BELONGIE S, MALIK J, PUZICHA J. Shape matching and object recognition using shape contexts[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2002, 24(4):509-522.
[17]	艾廷华. 深度学习赋能地图制图的若干思考[J]. 测绘学报, 2021, 50(9):1170-1182. DOI: 10.11947/j.AGCS.2021.20210091.
	AI Tinghua. Some thoughts on deep learning enabling cartography[J]. Acta Geodaetica et Cartographica Sinica, 2021, 50(9):1170-1182. DOI: 10.11947/j.AGCS.2021.20210091.
[18]	马磊, 闫浩文, 王中辉, 等. 机器自监督学习的建筑物面要素几何形状度量[J]. 测绘科学, 2017, 42(12):171-177.
	MA Lei, YAN Haowen, WANG Zhonghui, et al. Geometry shape measurement of building surface elements based on self-supervised machine learning[J]. Science of Surveying and Mapping, 2017, 42(12):171-177.
[19]	焦洋洋, 刘平芝, 刘爱龙, 等. AlexNet支持下的地图建筑物形状分类方法[J]. 地球信息科学学报, 2022, 24(12):2333-2341.
	JIAO Yangyang, LIU Pingzhi, LIU Ailong, et al. Map building shape classification method based on Alexnet[J]. Journal of Geo-information Science, 2022, 24(12):2333-2341.
[20]	HOCHREITER S, SCHMIDHUBER J. Long short-term memory[J]. Neural Computation, 1997, 9(8):1735-1780.
[21]	刘鹏程, 黄欣, 马宏然, 等. 建筑物多边形高精度识别的傅里叶形状描述子神经网络方法[J]. 测绘学报, 2022, 51(9):1969-1976. DOI: 10.11947/j.AGCS.2022.20210730.
	LIU Pengcheng, HUANG Xin, MA Hongran, et al. Fourier descriptor-based neural network method for high-precision shape recognition of building polygon[J]. Acta Geodaetica et Cartographica Sinica, 2022, 51(9):1969-1976. DOI: 10.11947/j.AGCS.2022.20210730.
[22]	NIEPERT M, AHMED M, KUTZKOV K, Learning convolutional neural networks for graphs[C]//Proceedings of the 33rd International Conference on Machine Learning. New York: Curran Associates, 2016: 2014-2023.
[23]	KIPF T N, WELLING M. Semi-supervised classification with graph convolutional networks[EB/OL]. [2024-05-30]. https://arxiv.org/abs/1609.02907v4.
[24]	于洋洋, 贺康杰, 武芳, 等. 面状居民地形状分类的图卷积神经网络方法[J]. 测绘学报, 2022, 51(11):2390-2402. DOI: 10.11947/j.AGCS.2022.20210134.
	YU Yangyang, HE Kangjie, WU Fang, et al. Graph convolution neural network method for shape classification of areal settlements[J]. Acta Geodaetica et Cartographica Sinica, 2022, 51(11):2390-2402. DOI: 10.11947/j.AGCS.2022.20210134.
[25]	YANG Chengzhuan, WEI Hui, YU Qian. A novel method for 2D nonrigid partial shape matching[J]. Neurocomputing, 2018, 275:1160-1176.
[26]	ZHANG Dengsheng, LU Guojun. Study and evaluation of different Fourier methods for image retrieval[J]. Image and Vision Computing, 2005, 23(1):33-49.
[27]	安晓亚, 孙群, 肖强, 等. 一种形状多级描述方法及在多尺度空间数据几何相似性度量中的应用[J]. 测绘学报, 2011, 40(4):495-501, 508.
	AN Xiaoya, SUN Qun, XIAO Qiang, et al. A shape multilevel description method and application in measuring geometry similarity of multi-scale spatial data[J]. Acta Geodaetica et Cartographica Sinica, 2011, 40(4):495-501, 508.
[28]	程绵绵, 孙群, 徐立, 等. 面轮廓线相似性和复杂性度量及在化简中的应用[J]. 测绘学报, 2019, 48(4):489-501. DOI: 10.11947/j.AGCS.2019.20180124.
	CHENG Mianmian, SUN Qun, XU Li, et al. Polygon contour similarity and complexity measurement and application in simplification[J]. Acta Geodaetica et Cartographica Sinica, 2019, 48(4):489-501. DOI: 10.11947/j.AGCS.2019.20180124.
[29]	HAMMOND D K, VANDERGHEYNST P, GRIBONVAL R. Wavelets on graphsvia spectral graph theory[J]. Applied and Computational Harmonic Analysis, 2011, 30(2):129-150.
[30]	LI Pengbo, YAN Haowen, LU Xiaomin. A siamese neural network for learning the similarity metrics of linear features[J]. International Journal of Geographical Information Science, 2023, 37(3):684-711.
[31]	VAN DER MAATEN L, HINTON G. Visualizing data using t-SNE[J]. Journal of Machine Learning Research, 2008, 9(1):2579-2605.

参考样本	K值	对比样本
参考样本	K值
	1	1.0	1.0	1.0	1.0	1.0
	2	1.0	1.0	1.0	1.0	1.0

参考样本	K值	对比样本
参考样本	K值
	1	0.978 7	0.920 6	0.983 4	0.943 3	0.932 5
	2	0.991 6	0.956 4	0.988 6	0.955 4	0.965 5

参考样本	K值	对比样本
参考样本	K值
	1	0.978 7	0.943 3	0.983 4	0.920 6	0.996 9
	2	0.991 6	0.955 4	0.988 6	0.956 4	0.998 3

方法	分类精度
ED	17.2
TA	79.5
GACE	94.1
NN	96.4
Spatial－GCN	92.3
Spectral－GCN(K=1)	97.6
Spectral－GCN(K=2)	98.1

检索结果	检索模板
检索结果
Top 1
Top 1	0.996 1	0.923 2	0.936 3	0.963 1	0.960 0	0.929 5
Top 2
Top 2	0.993 4	0.916 8	0.936 1	0.938 0	0.923 3	0.903 0
Bottom 1
Bottom 1	0.630 9	0.541 0	0.591 9	0.619 7	0.674 1	0.566 0
Bottom 2
Bottom 2	0.634 6	0.546 1	0.629 5	0.620 2	0.709 6	0.566 4

融合全局和局部特征的建筑物形状智能分类方法

An intelligent classification method for building shape based on fusion of global and local features

RichHTML

PDF

可视化

摘要/Abstract

引用本文

使用本文

图/表 15

参考文献 31

相关文章 15

编辑推荐

Metrics

本文评价

[1]	鄢薪, 慎利, 潘俊杰, 戴延帅, 王继成, 郑晓莉, 李志林. 多尺度特征融合与空间优化的弱监督高分遥感建筑变化检测[J]. 测绘学报, 2024, 53(8): 1586-1597.
[2]	徐涛, 杨元维, 高贤君, 王志威, 潘越, 李少华, 许磊, 王艳军, 刘波, 余静, 吴凤敏, 孙浩宇. 融合图卷积与多尺度特征的接触网点云语义分割[J]. 测绘学报, 2024, 53(8): 1624-1633.
[3]	林云浩, 王艳军, 李少春, 蔡恒藩. 一种耦合DeepLab与Transformer的农作物种植类型遥感精细分类方法[J]. 测绘学报, 2024, 53(2): 353-366.
[4]	张彩丽, 向隆刚, 李雅丽, 高松峰, 潘传姣. 路段级导航属性信息挖掘[J]. 测绘学报, 2024, 53(2): 367-378.
[5]	肖天元, 艾廷华, 余华飞, 杨敏, 刘鹏程. 地图综合图卷积神经网络点群简化方法[J]. 测绘学报, 2024, 53(1): 158-172.
[6]	安晓亚, 朱余德, 晏雄锋. 卷积神经网络支持下的建筑物选取方法[J]. 测绘学报, 2023, 52(9): 1574-1583.
[7]	宋佳璇, 范大昭, 董杨, 纪松, 李东子. 神经网络学习与灰度信息结合的跨视角影像线特征匹配算法[J]. 测绘学报, 2023, 52(6): 990-999.
[8]	赵冰冰, 谭骁勇, 杨学习, 石岩, 邓敏. 融合地理条件驱动效应和图卷积的土地利用演化模拟CA模型[J]. 测绘学报, 2023, 52(5): 831-842.
[9]	余东行, 徐青, 赵传, 郭海涛, 卢俊, 林雨准, 刘相云. 注意力引导特征融合与联合学习的遥感影像场景分类[J]. 测绘学报, 2023, 52(4): 624-637.
[10]	刘帅, 李笑迎, 于梦, 邢光龙. 高分辨率遥感图像双解耦语义分割网络模型[J]. 测绘学报, 2023, 52(4): 638-647.
[11]	杨必胜, 陈驰, 董震. 面向智能化测绘的城市地物三维提取[J]. 测绘学报, 2022, 51(7): 1476-1484.
[12]	于洋洋, 贺康杰, 武芳, 许俊奎. 面状居民地形状分类的图卷积神经网络方法[J]. 测绘学报, 2022, 51(11): 2390-2402.
[13]	姚群力, 胡显, 雷宏. 基于多尺度融合特征卷积神经网络的遥感图像飞机目标检测[J]. 测绘学报, 2019, 48(10): 1266-1274.
[14]	李牧闲, 桂志鹏, 成晓强, 吴华意, 秦昆. 多核学习与用户反馈结合的WMS图层检索方法[J]. 测绘学报, 2019, 48(10): 1320-1330.
[15]	曹云刚, 王志盼, 慎利, 肖雪, 杨磊. 像元与对象特征融合的高分辨率遥感影像道路中心线提取[J]. 测绘学报, 2016, 45(10): 1231-1240.