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作 者:宋晓倩 张学艺[2] 张春梅[1] 李万春 SONG Xiao-qian;ZHANG Xue-yi;ZHANG Chun-mei;LI Wan-chun(School of Computer Science and Engineering,North Minzu University,Yinchuan 750021,China;Key Laboratory of Characteristic Agrometeorological Disaster Monitoring and Early Warning and Risk Management in Arid Regions,CMA/Key Laboratory for Meteorological Disaster Prevention and Reduction of Ningxia,Yinchuan 750002,China)
机构地区:[1]北方民族大学计算机科学与工程学院,宁夏银川750021 [2]中国气象局旱区特色农业气象灾害监测预警与风险管理重点实验室/宁夏气象防灾减灾重点实验室,宁夏银川750002
出 处:《江苏农业学报》2020年第3期689-693,共5页Jiangsu Journal of Agricultural Sciences
基 金:国家自然科学基金项目(61461002);宁夏回族自治区青年拔尖人才培养工程项目(RQ0033);宁夏高等学校一流学科建设(电子科学与技术学科)项目(NXYLXK2-017A07);宁夏回族自治区重点研发计划一般项目(2019BDE03011);研究生创新项目(YCX18059)。
摘 要:为利用遥感手段快速、精准提取宁夏贺兰山东麓酿酒葡萄种植信息,提出了一种基于深度迁移学习的酿酒葡萄种植信息提取方法。该方法以全卷积神经网络(Fully convolutional networks,FCN)为基础,利用高分二号卫星遥感资料,以地面采集样本数据进行网络模型训练,利用迁移学习方法将训练好的网络模型迁移到FCN网络模型中,对其进行初始化,避免过拟合问题的发生,其网络训练验证集准确率高达88.16%,较传统的基于深度学习方法准确率提高7.17个百分点。结果表明,基于深度迁移学习的贺兰山东麓酿酒葡萄种植信息提取检测准确率可达91.93%,检测召回率达到91.15%。In order to extract the wine grape planting information of the eastern foot of Helan mountain by using remote sensing,a method based on deep transfer learning was proposed.On the basis of fully convolutional network(FCN),this method used the GF-2 remote sensing data to collect the sample data for the information extraction training.The trained network model was transferred to the FCN model by using the transfer learnin method.It was initialized in the network model to avoid over-fitting problem.The accuracy rate of its network training validation set was as high as 88.16%,which was 7.17 percentage points higher than that of traditional deep learning methods.The results showed that the accuracy rate of wine grape planting information extraction based on deep transfer learning at the eastern foot of Helan Mountain could reach 91.93%,and the recall rate reached 91.15%.
关 键 词:酿酒葡萄 信息提取 高分数据 深度学习 迁移学习
分 类 号:S127[农业科学—农业基础科学]
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