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

doi:10.14358/PERS.23-00036R2

CORC > 烟台海岸带研究所 > 中国科学院烟台海岸带研究所 > 中科院海岸带环境过程与生态修复重点实验室

	Rice Identification Under Complex Surface Conditions with CNN and Integrated Remote Sensing Spectral-Temporal-Spatial Features
	Liu, Tianjiao 2; Duan, Sibo 2; Chen, Jiankui 5; Zhang, Li 4; Li, Dong 3; Li, Xuqing 1
刊名	Photogrammetric Engineering and Remote Sensing
	2023
卷号	89 期号:12 页码:741-752
关键词	Complex networks Convolutional neural networks Neural network models Support vector machines
ISSN号	00991112
DOI	10.14358/PERS.23-00036R2
英文摘要	Accurate and effective rice identification has great significance for the sustainable development of agricultural management and food secu-rity. 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. © 2023 American Society for Photogrammetry and Remote Sensing.
语种	英语
内容类型	期刊论文
源URL	[http://ir.yic.ac.cn/handle/133337/34292]
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
作者单位	1.North China Institute of Aerospace Engineering, Langfang; 065000, China 2.Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing, China; 3.Yantai Institute of Coastal Zone Research, Chinese Academy of Sciences, Yantai, China; 4.College of Big Data and Information Engineering, GuiZhou University, GuiYang, China; 5.School of Artificial Intelligence, Hebei Oriental University, Langfang, 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):741-752.
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),741-752.
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):741-752.