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  2. 烟台海岸带研究所
  3. 中国科学院烟台海岸带研究所
  4. 中科院海岸带环境过程与生态修复重点实验室
Rice Identification Under Complex Surface Conditions with CNN and Integrated Remote Sensing Spectral-Temporal-Spatial Features

文献类型：期刊论文

作者Liu, Tianjiao1; Duan, Sibo1; Chen, Jiankui3; Zhang, Li4; Li, Dong5; Li, Xuqing2
刊名PHOTOGRAMMETRIC ENGINEERING AND REMOTE SENSING
出版日期2023-12-01
卷号89期号:12页码:64
ISSN号0099-1112
DOI10.14358/PERS.23-00036R2
通讯作者Duan, Sibo(duansibo@caas.cn)
英文摘要Accurate and effective rice identification has great significance for the sustainable development of agricultural management and food security. This paper proposes an accurate rice identification method that can solve the confused problem between fragmented rice fields and the surroundings in complex surface areas. The spectral, temporal, and spatial features extracted from the created Sentinel-2 time series were integrated and collaboratively displayed in the form of visual images, and a convolutional neural network model embedded with integrated information was established to further mine the key information that distinguishes rice from other types. The results showed that the overall accuracy, precision, recall, and F1-score of the proposed method for rice identification reached 99.4%, 99.5%, 99.5%, and 99.5%, respectively, achieving a better performance than the support vector machine classifier. Therefore, the proposed method can effectively reduce the confusion between rice and other types and accurately extract rice distribution information under complex surface conditions.
WOS关键词TEXTURAL FEATURES ; CLASSIFICATION ; AREA ; MODIS ; BAND ; PERFORMANCE ; LANDSAT ; INDEXES ; IMAGERY ; FIELDS
WOS研究方向Physical Geography ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
语种英语
WOS记录号WOS:001127821800003
资助机构Hebei Provincial Natural Science Foundation Project ; Major Project of High-Resolution Earth Observation System ; Doctoral Research Project
源URL[http://ir.yic.ac.cn/handle/133337/36296]  
专题烟台海岸带研究所_中科院海岸带环境过程与生态修复重点实验室
烟台海岸带研究所_海岸带信息集成与综合管理实验室
通讯作者Duan, Sibo
作者单位1.Chinese Acad Agr Sci, Inst Agr Resources & Reg Planning, Beijing, Peoples R China
2.North China Inst Aerosp Engn, Langfang 065000, Peoples R China
3.Hebei Oriental Univ, Sch Artificial Intelligence, Langfang, Peoples R China
4.GuiZhou Univ, Coll Big Data & Informat Engn, Guiyang, Peoples R China
5.Chinese Acad Sci, Yantai Inst Coastal Zone Res, Yantai, Peoples R China
推荐引用方式
GB/T 7714		 Liu, Tianjiao,Duan, Sibo,Chen, Jiankui,et al. Rice Identification Under Complex Surface Conditions with CNN and Integrated Remote Sensing Spectral-Temporal-Spatial Features[J]. PHOTOGRAMMETRIC ENGINEERING AND REMOTE SENSING,2023,89(12):64.
APA Liu, Tianjiao,Duan, Sibo,Chen, Jiankui,Zhang, Li,Li, Dong,&Li, Xuqing.(2023).Rice Identification Under Complex Surface Conditions with CNN and Integrated Remote Sensing Spectral-Temporal-Spatial Features.PHOTOGRAMMETRIC ENGINEERING AND REMOTE SENSING,89(12),64.
MLA Liu, Tianjiao,et al."Rice Identification Under Complex Surface Conditions with CNN and Integrated Remote Sensing Spectral-Temporal-Spatial Features".PHOTOGRAMMETRIC ENGINEERING AND REMOTE SENSING 89.12(2023):64.

入库方式： OAI收割

来源：烟台海岸带研究所

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