| 栅格DEM微地形分类的BP神经网络法 |
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| 引用本文： | 周访滨,邹联华,张晓炯,孟凡一.栅格DEM微地形分类的BP神经网络法[J].测绘通报,2019,0(10):101-104,132. |
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| 作者姓名： | 周访滨 邹联华 张晓炯 孟凡一 |
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| 作者单位： | 长沙理工大学交通运输工程学院,湖南 长沙410114;长沙理工大学特殊环境道路工程湖南省重点实验室,湖南 长沙410114;长沙理工大学交通运输工程学院,湖南 长沙,410114 |
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| 基金项目： | 国家自然科学基金面上项目（41671446）；长沙理工大学特殊环境道路工程湖南省重点实验室开放基金（kfj140502） |
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| 摘 要： | 栅格DEM微地形分类是数字地形精细化应用的基础，基于规则化知识的栅格DEM微地形分类方法存在自动化程度低、分类残缺等问题。本文利用BP神经网络的优势构建了栅格DEM微地形分类的人工智能方法与实现途径。以山体部位分类为微地形分类典型样例进行试验验证与分析，试验结果表明，栅格DEM微地形分类的BP神经网络法较已有的地形因子叠加分析方法存在明显优势，不仅在流程上可避免烦琐的数据叠加分析过程，而且分类结果的完整性和错分率都得到有效改善；在山体部位分出的6种微地形中，冲积地对该方法适应性最强，准确率为100%，背坡的适应性最弱准确率为89.23%。
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| 关 键 词： | 栅格DEM 地形分类 BP神经网络 山体部位 |
| 收稿时间： | 2018-11-08 |
Micro landform classification method of grid DEM based on BP neural network |
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| ZHOU Fangbin,ZOU Lianhua,ZHANG Xiaojiong,MENG Fanyi.Micro landform classification method of grid DEM based on BP neural network[J].Bulletin of Surveying and Mapping,2019,0(10):101-104,132. |
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| Authors: | ZHOU Fangbin ZOU Lianhua ZHANG Xiaojiong MENG Fanyi |
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| Institution: | 1. School of Traffic & Transportation Engineering, Changsha University of Science & Technology, Changsha 410114, China;2. Key Laboratory of Special Environment Road Engineering of Hunan Province, Changsha University of Science & Technology, Changsha 410114, China |
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| Abstract: | Micro landform classification of grid DEM is the foundation of digital landform refinement application. The micro landform classification method of grid DEM based on regular knowledge has problems such as low degree of automation and incomplete classification. With the advantages of BP neural network, an artificial intelligence method and implementation approach for micro landform classification of grid DEM are constructed. The experimental results show that the BP neural network method of micro landform classification of grid DEM has the advantages over the existing method of overlay analysis by landform factors. The BP neural network method of micro landform classification of grid DEM not only avoids the complicated data overlay analysis process, but also effectively improves the completeness and misclassification. Among the six kinds of micro landform classified from the hill-position, the alluvium has the strongest adaptability to this method, with accuracy of 100% and the weakest accuracy of 89.23% for the back-slope. |
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| Keywords: | grid DEM landform classification BP neural network hill-position |
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