摘要
遥感技术能够快速准确地获取农作物空间分布信息,为探究2021年黄淮海平原冬小麦空间分布信息,基于Google Earth Engine(GEE)云平台,以Sentinel-1 SAR雷达影像和Sentienl-2光学遥感影像为数据源,通过计算极化特征、光谱特征和纹理特征,运用随机森林等4种机器学习方法和深度循环神经网络模型,对研究区冬小麦空间分布信息进行提取,并对比各分类器和网络架构的分类精度。结果表明,黄淮海平原冬小麦总面积约为16226667 hm^(2),占研究区总面积的49.17%,其中冬小麦种植面积最大的是河南省,约为4647334 hm^(2),研究区冬小麦种植分布呈现由东向西、由南向北递减的趋势;随机森林是4种机器学习方法中识别精度最高的分类器,总体分类精度为94.30%;在随机森林算法中仅使用Sentinel-1雷达数据总体精度为87.38%,仅使用Sentinel-2光学数据总体精度为93.95%,而融合时序Sentinel主被动遥感数据总体精度为94.30%;在大范围的冬小麦分类上,深度学习模型的泛化性高于机器学习方法。
Current remote sensing technology can quickly and accurately obtain the spatial distribution information of crops.In order to explore the spatial distribution information of winter wheat in the Huang-Huai-Hai Plain in 2021,based on the Google Earth Engine(GEE) cloud platform.Sentinel-1 SAR radar image and Sentienl-2 optical remote sensing image were used as data sources,the spatial distribution information of winter wheat in the study area was extracted by computing polarization characteristics,spectral characteristics and texture characteristics,using four machine learning methods and deep learning network model.The classification accuracy of each classifier and network architecture was compared.The results showed that the total area of winter wheat in the Huang-Huai-Hai Plain was 16 226 667 hm^(2),accounting for 49.17% of total area of the study area.The winter wheat planting area was the largest in Henan Province,accounting for 4 647 334 hm^(2).The winter wheat planting distribution in the study area showed a decreasing trend from east to west and from south to north.Random forest was the classifier with the highest recognition accuracy among the four machine learning methods,with an overall classification accuracy of 94.30%.In the random forest algorithm,the overall accuracy of only using Sentinel-1 radar data was 87.38%,and the overall accuracy of only using Sentinel-2 optical data was 93.95%,while the overall accuracy of the fusion sequence Sentinel active and passive remote sensing data was 94.30%.In a wide range of winter wheat classification,the generalization of deep learning model was higher than that of machine learning.
作者
冯权泷
任燕
姚晓闯
牛博文
陈泊安
赵圆圆
FENG Quanlong;REN Yan;YAO Xiaochuang;NIU Bowen;CHEN Boan;ZHAO Yuanyuan(College of Land Science and Technology,China Agricultural University,Beijing 100193,China;Key Laboratory for Agricultural Land Quality Monitoring and Control,Ministry of Natural Resources,Beijing 100193,China)
出处
《农业机械学报》
EI
CAS
CSCD
北大核心
2023年第2期160-168,共9页
Transactions of the Chinese Society for Agricultural Machinery
基金
国家超级计算郑州中心创新生态系统建设科技专项(201400210100)
国家自然科学基金项目(42001367)。
作者简介
冯权泷(1988-),男,副教授,博士生导师,主要从事机器学习、深度学习在农业遥感应用研究,E⁃mail:fengql@cau.edu.cn;通信作者:姚晓闯(1986-),男,副教授,博士生导师,主要从事空间数据组织、管理与分析等研究,E⁃mail:yxc@cau.edu.cn。
