| 一种深度卷积神经网络土地利用场景照片的分类方法 |
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| 引用本文： | 徐世武,曾珏,张诗慧,李长征,李亭谕.一种深度卷积神经网络土地利用场景照片的分类方法[J].测绘通报,2020,0(2):24. |
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| 作者姓名： | 徐世武 曾珏 张诗慧 李长征 李亭谕 |
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| 作者单位： | 1. 中国地质大学(武汉), 湖北 武汉 430074;2. 中国国土勘测规划院, 北京 100035 |
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| 基金项目： | 核查成果自动判别技术研究项目（2019114017） |
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| 摘 要： | 国土调查多角度实景举证照片具有视角多、分辨率高、层次丰富和剖面清晰的特点，透视且细致地刻画了土地利用图斑赋存状况和场景，弥补了遥感影像单一天顶视角的不足。本文基于语义分割提出了一种深度卷积神经网络（DCNN）实景照片土地利用场景分类方法，多语义标记照片场景，语义组合智能判定照片土地利用类别。该方法成功地应用在第三次国土调查照片自动核查工作中，减轻了人工判读工作量，提高了土地利用场景自动识别的精度。
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| 关 键 词： | 国土调查 实景照片 深度卷积神经网络 多语义标记 语义组合 土地利用场景分类 |
| 收稿时间： | 2019-11-20 |
A method of deep convolutional neural network photo classification for landuse scenes |
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| XU Shiwu,ZENG Jue,ZHANG Shihui,LI Changzheng,LI Tingyu.A method of deep convolutional neural network photo classification for landuse scenes[J].Bulletin of Surveying and Mapping,2020,0(2):24. |
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| Authors: | XU Shiwu ZENG Jue ZHANG Shihui LI Changzheng LI Tingyu |
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| Institution: | 1. School of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, China;2. Chinese Land Surveying and Planning Institute, Beijing 100035, China |
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| Abstract: | The multi-angle real-life proofed photos of the national land survey have multiple characteristics:high resolution, rich layers, and clear sections. The photos are accurate and detailed depictions of the occurrence status and scenes of landuse maps, which makes up for the lack of ground perspective of remote sensing images. Based on semantic segmentation, this paper proposes an innovative landuse scene classification method with a deep convolutional neural network (DCNN) for ground perspective photos. The method annotates photo scenes with multi-semantic technique and determines categories of photos of landuse with semantic composition. Successfully applied in the third automatic survey of land survey photos, this method reduces the workload of manual interpretation and improved the accuracy of automatic identification of landuse scenes. |
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| Keywords: | land survey real photos deep convolutional neural networks multiple semantic annotations semantic composition land use scenario classification |
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