| 基于Siamese卷积神经网络的影像瓦片变化检测技术 |
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| 引用本文： | 万冉冉,陈娟,廖明伟,刘异,庞超.基于Siamese卷积神经网络的影像瓦片变化检测技术[J].测绘通报,2020,0(4):96. |
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| 作者姓名： | 万冉冉 陈娟 廖明伟 刘异 庞超 |
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| 作者单位： | 1. 江西省基础地理信息中心, 江西 南昌 330029;2. 流域生态与地理环境监测国家测绘地理信息局重点实验室, 江西 南昌 330029;3. 武汉大学测绘学院, 湖北 武汉 430079 |
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| 基金项目： | 江西省科技计划（20171BBE50062）；国家测绘地理信息局公益性行业科研专项（201512026） |
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| 摘 要： | 针对地理信息变化较快而传统更新方式效率不高的问题，目前许多学者提出了各类变化检测的方法，但这些方法大都是基于影像数据进行试验，对影像预处理要求较高，且检测精度的稳定性较差，受数据源质量影响较大。而天地图、百度地图、谷歌地图等地图中均可免费下载各种级别的影像瓦片，因此本文提出利用天地图影像瓦片进行试验，采用Siamese卷积神经网络（SCNN）和深度学习技术，开发基于SCNN的高精度变化监测算法，以快速发现变化区域，实现地理信息变化信息检测。
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| 关 键 词： | 影像瓦片 Siamese卷积神经网络 深度学习 变化检测 天地图 |
| 收稿时间： | 2019-03-18 |
The technology of image tile change detection based on Siamese convolutional neural network |
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| WAN Ranran,CHEN Juan,LIAO Mingwei,LIU Yi,PANG Chao.The technology of image tile change detection based on Siamese convolutional neural network[J].Bulletin of Surveying and Mapping,2020,0(4):96. |
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| Authors: | WAN Ranran CHEN Juan LIAO Mingwei LIU Yi PANG Chao |
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| Institution: | 1. Jiangxi Provincial Geomatics Center, Nanchang 330029, China;2. Key Laboratory of Watershed Ecology and Geographical Environment Monitoring, NASG, Nanchang 330029, China;3. School of Geodesy and Geomatics, Wuhan University, Wuhan 430079, China |
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| Abstract: | In view of the fast change of geographic information and the inefficiency of traditional updating methods, many scholars have proposed various kinds of change detection methods, but most of these methods experiments are based on image data, which require higher image preprocessing requirements, and the detection accuracy is always unstability, which is greatly affected by the quality of data sources. While in Tianditu maps, Baidu maps, Google maps and other electronic maps, several levels of image tiles can be downloaded free of charge. Therefore, this paper proposes to use Tianditu map image tiles for experiment, use Siamese convolution neural network (SCNN) and deep learning technology, develop a high-precision change monitoring algorithm based on SCNN, quickly find the change area, and realize the change information detection of geographic information. |
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| Keywords: | image map tiles Siamese convolution neural network deep learning change detection Tianditumaps |
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