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国土资源遥感  2020, Vol. 32 Issue (1): 35-42    DOI: 10.6046/gtzyyg.2020.01.06
     技术方法 本期目录 | 过刊浏览 | 高级检索 |
U-net模型在高分辨率遥感影像水体提取中的应用
王宁1,程家骅2(),张寒野2,曹红杰1,3,刘军3
1. 北京合众思壮科技股份有限公司,北京 100015
2. 中国水产科学研究院东海水产研究所,上海 200090
3. 北斗导航位置服务(北京)有限公司,北京 100191
Application of U-net model to water extraction with high resolution remote sensing data
Ning WANG1,Jiahua CHENG2(),Hanye ZHANG2,Hongjie CAO1,3,Jun LIU3
1. Beijing Unistrong Science and Technology Co., Ltd., Beijing 100015, China
2. East China Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences, Shanghai 200090, China
3. BeiDou Navigation and LBS (Beijing) Co., Ltd., Beijing 100191, China
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摘要 

选择安徽省巢湖流域为研究区,采用U-net模型和随机森林模型,对高分一号(GF-1)高分辨率遥感影像进行水体信息提取,并对比分析了2种模型的水体提取结果和效率。结果表明: ①对于大面积水体,2种模型的水体提取结果均具有较高的精度; ②对于小面积水体,随机森林模型水体提取结果存在较多细碎图斑,U-net模型水体提取结果和人工目视解译结果更加符合; ③对于遥感影像中城市建筑物阴影和山体阴影,U-net模型能较好地消除阴影影响,正确提取水体,而随机森林模型存在较多将阴影误分为水体的现象; ④总体来看,在复杂地表覆盖类型条件下,U-net模型提取水体的总体精度为98.69%,Kappa系数为0.95,均高于随机森林模型,在2种模型漏分误差相当的情况下,U-net模型错分误差远小于随机森林模型。U-net模型避免了人工提取分类特征的过程,自动化程度更高,训练效率较高,适用于遥感影像中水体高精度提取。

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王宁
程家骅
张寒野
曹红杰
刘军
关键词 GF-1U-net模型随机森林水体提取    
Abstract

In this paper, the authors used a U-net model to conduct water extraction, and the result was compared with that of the random forest model. The accuracy of the U-net model was validated by using GF-1 images in Chaohu Lake Basin. The results show that both models are of high accuracy for large area of water body, but random forest model has more spots for small area of water body, and the result of U-net model is more consistent with the manual visual interpretation result. Moreover, the U-net model can effectively remove the shadows of mountains and buildings. The result indicates that U-net model performs better than random forest model with the overall accuracy of 98.69%, Kappa coefficient of 0.95, omission error of 1.90% and commission error of 1.18%. In contrast, the overall accuracy, Kappa coefficient, omission error and commission error of random forest model are about 98.05%, 0.92, 1.61% and 2.99%, respectively. In addition, the classification features for traditional machine learning model are always calculated by manual extraction. However, the inputs for U-net model are the 4 band spectrum data of GF-1 images. These data suggest that the U-net model avoids the process of manually extracting classification features and has a higher degree of automation. It should be noted that the U-net model uses more train samples with less time-consuming. It is believed that this model can significantly improve the surface water detection accuracy and can be used for the automatic renewal of a larger range of water bodies.

Key wordsGF-1    U-net    random forest    water extraction
收稿日期: 2019-02-22      出版日期: 2020-03-14
ZTFLH:  TP79  
基金资助:国家重点研发计划项目“全球位置信息叠加协议与位置服务网技术”(编号: 2017YFB0503700);北京市博士后工作经费资助项目“基于深度学习的高分辨率遥感影像养殖水体提取技术研究”(编号: 2018-ZZ-036);青海省重大科技专项项目“海北州高寒草地生态畜牧业大数据管理平台与关键技术集成示范”(编号: 2017-NK-A4);农财专项-农业农村资源等监测统计经费项目(2017)
通讯作者: 程家骅     E-mail: dhsziyuan@163.com
作者简介: 王 宁(1986-),男,在站博士后,主要从事遥感影像智能化处理研究。Email: remote_gis@163.com。
引用本文:   
王宁,程家骅,张寒野,曹红杰,刘军. U-net模型在高分辨率遥感影像水体提取中的应用[J]. 国土资源遥感, 2020, 32(1): 35-42.
Ning WANG,Jiahua CHENG,Hanye ZHANG,Hongjie CAO,Jun LIU. Application of U-net model to water extraction with high resolution remote sensing data. Remote Sensing for Land & Resources, 2020, 32(1): 35-42.
链接本文:  
http://www.gtzyyg.com/CN/10.6046/gtzyyg.2020.01.06      或      http://www.gtzyyg.com/CN/Y2020/V32/I1/35
Fig.1  研究区域位置
Fig.2  U-net模型结构
评价指标 计算方法
总体精度 正确分类像元数占总像元数的比例
Kappa系数 Kappa=Nxii-(xi·x·i)N2-(xi·x·i)
漏分误差 水体错分为非水体的像元数占真实水体总像元数的比例
错分误差 非水体错分为水体的像元数占分类得到水体总像元数的比例
Tab.1  遥感影像分类结果评价指标
Fig.3  水体提取结果对比
Fig.4  小面积水体提取结果对比
Fig.5  U-net模型和随机森林模型去除阴影结果对比
模型 总体精度/% Kappa 漏分误差/% 错分误差/%
U-net 98.69 0.95 1.90 1.18
随机森林 98.05 0.92 1.61 2.99
Tab.2  水体提取精度对比
模型 训练样本数/(像元×像元) 耗时/s
U-net 14 200×8 000 1 136
随机森林 14 200×100 14 126
Tab.3  不同模型提取水体效率
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