| 立交桥识别的CNN卷积神经网络法 |
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| 引用本文： | 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. |
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| 作者姓名： | 何海威 钱海忠 谢丽敏 段佩祥 |
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| 作者单位： | 信息工程大学地理空间信息学院, 河南 郑州 450000 |
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| 基金项目： | 国家自然科学基金，The National Natural Science Foundation of China |
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| 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。
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| 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 |
| 收稿时间： | 2017-05-18 |
| 修稿时间： | 2017-12-31 |
Interchange Recognition Method Based on CNN |
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| 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. |
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| Authors: | HE Haiwei QIAN Haizhong XIE Limin DUAN Peixiang |
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| Institution: | Institute of Geospatial Information, Information Engineering University, Zhengzhou 450000, China |
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| 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 thefuzzy characteristics of theinterchange,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. |
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