| 田琳静,宋文龙,卢奕竹,吕娟,李焕新,陈静.基于深度学习的农业区土地利用无人机监测分类[J].中国水利水电科学研究院学报,2019,17(4):312-320 |
| 基于深度学习的农业区土地利用无人机监测分类 |
| Rapid monitoring and classification of land use in agricultural areas by UAV based on deep learning method |
| 投稿时间：2019-06-03 |
| DOI：10.13244/j.cnki.jiwhr.2019.04.010 |
| 中文关键词: 农业干旱 无人机 土地利用 深度学习 卷积神经网络 遥感 YC-mapper |
| 英文关键词:agricultural drought UAV land use deep learning convolutional neural network (CNN) remote sensing YC-mapper |
| 基金项目:国家重点研发计划项目（2018YFC1508702，2016YFC0400106-2）；国家自然科学青年基金项目（51609259，41601569）；中国水利水电科学研究院专项（JZ0145B472016，JZ0145B862017）；水利部技术示范项目（SF-201703） |
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| 中文摘要: |
| 农业种植区土地利用快速监测与分类对政府部门制定规划、土地资源管理、生态环境保护规划与决策以及农业旱情与旱灾动态监测评估具有重要意义。本研究以东雷二期抽黄灌区具有下垫面代表性的小区域为研究区，利用卷积神经网络深度学习方法，针对较高空间分辨率的无人机航片影像，开展了农业区土地利用监测分类研究，并与最大似然法进行比较，探究该方法对于农业区土地利用监测分类的适用性。结果表明，该方法优于最大似然法，其总体分类精度达93%以上，Kappa系数为0.9以上，能够更清晰地识别提取出地物边界，分类效果较好。本研究有助于提升应急抗旱减灾工作对农业区土地利用的快速监测与分类能力，为旱情与旱灾快速监测评估、决策提供技术支持，同时能够及时为政府、土地资源管理以及生态环境保护规划等部门提供基础数据。 |
| 英文摘要: |
| The rapid monitoring and classification of land use in agricultural areas is important for land resource management, ecological environmental protection planning and decision-making, government department planning,the dynamic monitoring and assessment of agricultural drought. Based on the remote sensing images of the unmanned aerial vehicle (UAV) with higher spatial resolution, we chose small regions with the underlying surface representation of the Donglei Irrigated District (Phase Ⅱ) as the research area to carry out the monitoring of land use classification research in agriculture areas using the convolutional neural network (CNN) deep learning methods. In addition,the method is compared with the maximum likelihood method to explore the applicability of the method for land use monitoring classification in agricultural areas. The results show that the classification accuracy of this method is better than the maximum likelihood method. The overall classification accuracy is over 93%, and the Kappa coefficient is above 0.9. The boundary of the extracted features can be clearly identified and the classification effect is better. This study can help improve the rapid monitoring and classification of land use in agricultural areas during emergency drought and mitigation, and provide technical support for rapid monitoring and assessment of drought, which will provide basic data for the government, land resource management and ecological environmental protection planning in a timely manner. |
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