立交桥识别的CNN卷积神经网络法

doi:10.11947/j.AGCS.2018.20170265

测绘学报 ›› 2018, Vol. 47 ›› Issue (3): 385-395.doi: 10.11947/j.AGCS.2018.20170265

立交桥识别的CNN卷积神经网络法

何海威, 钱海忠, 谢丽敏, 段佩祥

信息工程大学地理空间信息学院, 河南郑州 450000

收稿日期:2017-05-18 修回日期:2017-12-31 出版日期:2018-03-20 发布日期:2018-03-29
通讯作者: 钱海忠 E-mail:qianhaizhong2005@163.com
作者简介:何海威(1991-),男,博士生,研究方向为自动制图综合、应急制图等。E-mail:adai928@126.com
基金资助:
国家自然科学基金（41571442；41171305）

Interchange Recognition Method Based on CNN

HE Haiwei, QIAN Haizhong, XIE Limin, DUAN Peixiang

Institute of Geospatial Information, Information Engineering University, Zhengzhou 450000, China

Received:2017-05-18 Revised:2017-12-31 Online:2018-03-20 Published:2018-03-29
Supported by:
The National Natural Science Foundation of China(Nos. 41571442;41171305)

摘要/Abstract

摘要： OSM数据中立交桥结构的识别和分类，能够为构建多尺度模型、导航和位置服务、拥堵分析等提供重要信息。传统的立交桥识别方法依赖于人工设计的低层次特征，无法有效区分存在干扰路段的复杂立交桥结构。本文针对当前算法抗差性上存在的不足，提出了一种新的基于卷积神经网络的立交桥识别方法。该方法将矢量数据与栅格图像相结合，利用神经网络学习区分立交桥类型的高层次模糊性特征，从而对OSM中的复杂立交桥结构进行分类。试验表明，该方法有较强的抗干扰性，在复杂的立交桥形态分类中取得了良好的效果，并随着案例库的扩充和神经网络模型的优化存在进一步提升的空间。

关键词: 制图综合, 立交桥识别, 卷积神经网络, 栅矢结合, 模式识别

Abstract: The identification and classification of interchange structures in OSM data can provide important information for the construction of multi-scale model, navigation and location services, congestion analysis, etc. The traditional method of interchange identification relies on the low-level characteristics of artificial design, and cannot distinguish the complex interchange structure with interference section effectively. In this paper, a new method based on convolutional neural network for identification of the interchange is proposed. The method combines vector data with raster image, and uses neural network to learn the fuzzy characteristics of the interchange, and classifies the complex interchange structure in OSM. Experiments show that this method has strong anti-interference, and has achieved good results in the classification of complex interchange shape, and there is room for further improvement with the expansion of the case base and the optimization of neural network model.

Key words: cartographic generalization, interchange recognition, convolutional neural network, grid vector combination, pattern recognition

中图分类号:

P208

何海威, 钱海忠, 谢丽敏, 段佩祥. 立交桥识别的CNN卷积神经网络法[J]. 测绘学报, 2018, 47(3): 385-395.

HE Haiwei, QIAN Haizhong, XIE Limin, DUAN Peixiang. Interchange Recognition Method Based on CNN[J]. Acta Geodaetica et Cartographica Sinica, 2018, 47(3): 385-395.

参考文献

[1] MACKANESS W A,MACKECHNIE G A.Automating the Detection and Simplification of Junctions in Road Networks[J].GeoInformatica,1999,3(2):185-200.
[2] 徐柱,蒙艳姿,李志林,等.基于有向属性关系图的典型道路交叉口结构识别方法[J].测绘学报,2011,40(1):125-131. XU Zhu,MENG Yanzi,LI Zhilin,et al.Recognition of Structures of Typical Road Junctions Based on Directed Attributed Relational Graph[J].Acta Geodaetica et Cartographica Sinica,2011,40(1):125-131.
[3] 王骁,钱海忠,丁雅莉,等.采用拓扑关系与道路分类的立交桥整体识别方法[J].测绘科学技术学报,2013,30(3):324-328. WANG Xiao,QIAN Haizhong,DING Yali,et al.The Integral Identification Method of Cloverleaf Junction Based on Topology and Road Classification[J].Journal of Geomatics Science and Technology,2013,30(3):324-328.
[4] 马超,孙群,陈换新,等.利用路段分类识别复杂道路交叉口[J].武汉大学学报(信息科学版),2016,41(9):1232-1237. MA Chao,SUN Qun,CHEN Huanxin,et al.Recognition of Road Junctions Based on Road Classification Method[J].Geomatics and Information Science of Wuhan University,2016,41(9):1232-1237.
[5] DONAHUE J,JIA Yangqing,VINYALS O,et al.DeCAF:A Deep Convolutional Activation Feature for Generic Visual Recognition[J].Computer Science,2013,50(1):815-830.
[6] JIA Yangqing,SHELHAMER E,DONAHUE J,et al.Caffe:Convolutional Architecture for Fast Feature Embedding[C]//Proceedings of the 22nd ACM International Conference on Multimedia.Orlando,Florida:ACM,2014:675-678.
[7] 何小飞,邹峥嵘,陶超,等.联合显著性和多层卷积神经网络的高分影像场景分类[J].测绘学报,2016,45(9):1073-1080.DOI:10.11947/j.AGCS.2016.20150612. HE Xiaofei,ZOU Zengrong,TAO Chao,et al.Combined Saliency with Multi-convolutional Neural Network for High Resolution Remote Sensing Scene Classification[J].Acta Geodaetica et Cartographica Sinica,2016,45(9):1073-1080.DOI:10.11947/j.AGCS.2016.20150612.
[8] ZHU Qiqi,ZHONG Yanfei,ZHAO Bei,et al.Bag-of-visual-Words Scene Classifier With Local and Global Features for High Spatial Resolution Remote Sensing Imagery[J].IEEE Geoscience and Remote Sensing Letters,2016,13(6):747-751.
[9] 刘栋,李素,曹志冬.深度学习及其在图像物体分类与检测中的应用综述[J].计算机科学,2016,43(12):13-23. LIU Dong,LI Su,CAO Zhidong.State-of-the-art on Deep Learning and Its Application in Image Object Classification and Detection[J].Computer Science,2016,43(12):13-23.
[10] LECUN Y,BOSER B,DENKER J S,et al.Backpropagation Applied to Handwritten Zip Code Recognition[J].Neural Computation,1989,1(4):541-551.
[11] HINTON G E,SALAKHUTDINOV R R.Reducing the Dimensionality of Data with Neural Networks[J].Science,2006,313(5786):504-507.
[12] LECUN Y,BENGIO Y,HINTON G.Deep Learning[J].Nature,2015,521(7553):436-444.
[13] DENG Li,YU Dong.Deep Learning:Methods and Applications[J].Foundations and Trends in Signal Processing,2014,7(3-4):197-387.
[14] 谢剑斌,兴军亮,张立宁,等.视觉机器学习20讲[M].北京:清华大学出版社,2015. XIE Jianbin,XING Junliang,ZHANG Lining,et al.20 Lectures On Visual Machine Learning[M].Beijing:Tsinghua University Press,2015.
[15] 周志华.机器学习[M].北京:清华大学出版社,2016. ZHOU Zhihua.Machine Learning[M].Beijing:Tsinghua University Press,2016.
[16] LECUN Y,BOTTOU L,BENGIO Y,et al.Gradient-based Learning Applied to Document Recognition[J].Proceedings of the IEEE,1998,86(11):2278-2324.
[17] KRIZHEVSKY A,SUTSKEVER I,HINTON G E.Image Net Classification with Deep Convolutional Neural Networks[C]//Proceedings of the 25th International Conference on Neural Information Processing Systems.Lake Tahoe,Nevada:ACM,2012:1097-1105.
[18] HEINZLE F,ANDERS K H,SESTER M.Pattern Recognition in Road Networks on the Example of Circular Road Detection[C]//RAUBAL M,MILLER H J,FRANK A U,et al.Geographic Information Science.Berlin,Heidelberg:Springer,2006:153-167.
[19] MACKANESS W A,RUAS A,SARJAKOSKI L T.Generalisation of Geographic Information:Cartographic Modelling and Applications[M].Amsterdam:Elsevier Science Ltd,2007:196-197.
[20] YANG Bisheng,LUAN Xuechen,LI Qingquan.An Adaptive Method for Identifying the Spatial Patterns in Road Networks[J].Computers,Environment and Urban Systems,2010,34(1):40-48.

立交桥识别的CNN卷积神经网络法

Interchange Recognition Method Based on CNN

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