Rice Identification Under Complex Surface Conditions with CNN and Integrated Remote Sensing Spectral-Temporal-Spatial Features

doi:10.14358/PERS.23-00036R2

	Rice Identification Under Complex Surface Conditions with CNN and Integrated Remote Sensing Spectral-Temporal-Spatial Features
	Liu, Tianjiao 1; Duan, Sibo 1; Chen, Jiankui2 ; Zhang, Li 3; Li, Dong 4; Li, Xuqing 5
发表期刊	PHOTOGRAMMETRIC ENGINEERING AND REMOTE SENSING
ISSN	0099-1112
	2023-12-01
卷号	89 期号:12 页码:64
DOI	10.14358/PERS.23-00036R2
通讯作者	Duan, Sibo([email protected])
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.
资助机构	Hebei Provincial Natural Science Foundation Project ; Major Project of High-Resolution Earth Observation System ; Doctoral Research Project
收录类别	SCI
语种	英语
关键词[WOS]	TEXTURAL FEATURES ; CLASSIFICATION ; AREA ; MODIS ; BAND ; PERFORMANCE ; LANDSAT ; INDEXES ; IMAGERY ; FIELDS
研究领域[WOS]	Physical Geography ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
WOS记录号	WOS:001127821800003
引用统计
文献类型	期刊论文
条目标识符	http://ir.yic.ac.cn/handle/133337/36297
专题	中国科学院海岸带环境过程与生态修复重点实验室中国科学院海岸带环境过程与生态修复重点实验室_海岸带信息集成与战略规划研究中心
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.Hebei Oriental Univ, Sch Artificial Intelligence, Langfang, Peoples R China 3.GuiZhou Univ, Coll Big Data & Informat Engn, Guiyang, Peoples R China 4.Chinese Acad Sci, Yantai Inst Coastal Zone Res, Yantai, Peoples R China 5.North China Inst Aerosp Engn, Langfang 065000, 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.