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Delineation of cultivated land parcels based on deep convolutional networks and geographical thematic scene division of remotely sensed images

文献类型：期刊论文

作者Xu, Lu1; Ming, Dongping1,2; Du, Tongyao1; Chen, Yangyang1; Dong, Dehui1; Zhou, Chenghu3
刊名COMPUTERS AND ELECTRONICS IN AGRICULTURE
出版日期2022
卷号192页码:16
关键词Cultivated land parcels Geographical Thematic scenes Agricultural remote sensing Semantic segmentation Deep learning
ISSN号0168-1699
DOI10.1016/j.compag.2021.106611
通讯作者Ming, Dongping(mingdp@cugb.edu.cn)
英文摘要Extraction of cultivated land information from high spatial resolution remote sensing images is increasingly becoming an important approach to digitization and informatization in modern agriculture. The continuous development of deep learning technology has made it possible to extract information of cultivated land parcels by an intelligent way. Aiming at fine extraction of cultivated land parcels within large areas, this article builds a framework of geographical thematic scene division according to the rule of territorial differentiation in geography. A deep learning semantic segmentation network, improved U-net with depthwise separable convolution (DSCUnet), is proposed to achieve the division of the whole image. Then, an extended multichannel richer convolutional features (RCF) network is involved to delineate the boundaries of cultivated land parcels from agricultural functional scenes obtained by the former step. In order to testify the feasibility and effectiveness of the proposed methods, this article implemented experiments using Gaofen-2 images with different spatial resolution. The results show an outstanding performance using methods proposed in this article in both dividing agricultural functional scenes and delineating cultivated land parcels compared with other commonly used methods. Meanwhile, the extraction results have the highest accuracy in both the traditional evaluation indices (like Precision, Recall, F-1, and IoU) and geometric boundary precision of cultivated land parcels. The methods in this article can provide a feasible solution to the problem of finely extracting cultivated land parcels information within large areas and complex landscape conditions in practical applications.
WOS关键词SEGMENTATION ; SCALE ; FEATURES ; CLASSIFICATION ; EXTRACTION ; SELECTION
资助项目National Natural Science of China[41671369] ; National Key Research and Development Program[2017YFB0503600-05] ; 2021 Graduate Innovation Fund Project of China University of Geo-sciences, Beijing[ZD2021YC054] ; Fundamental Research Funds for the Central Universities
WOS研究方向Agriculture ; Computer Science
语种英语
WOS记录号WOS:000754268400001
出版者ELSEVIER SCI LTD
资助机构National Natural Science of China ; National Key Research and Development Program ; 2021 Graduate Innovation Fund Project of China University of Geo-sciences, Beijing ; Fundamental Research Funds for the Central Universities
源URL[http://ir.igsnrr.ac.cn/handle/311030/170805]  
专题中国科学院地理科学与资源研究所
通讯作者Ming, Dongping
作者单位1.China Univ Geosci Beijing, Sch Informat Engn, Beijing 100083, Peoples R China
2.Minist Nat Resources China, Polytech Ctr Nat Resources Big Data, Beijing 100036, Peoples R China
3.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China
推荐引用方式
GB/T 7714		 Xu, Lu,Ming, Dongping,Du, Tongyao,et al. Delineation of cultivated land parcels based on deep convolutional networks and geographical thematic scene division of remotely sensed images[J]. COMPUTERS AND ELECTRONICS IN AGRICULTURE,2022,192:16.
APA Xu, Lu,Ming, Dongping,Du, Tongyao,Chen, Yangyang,Dong, Dehui,&Zhou, Chenghu.(2022).Delineation of cultivated land parcels based on deep convolutional networks and geographical thematic scene division of remotely sensed images.COMPUTERS AND ELECTRONICS IN AGRICULTURE,192,16.
MLA Xu, Lu,et al."Delineation of cultivated land parcels based on deep convolutional networks and geographical thematic scene division of remotely sensed images".COMPUTERS AND ELECTRONICS IN AGRICULTURE 192(2022):16.

入库方式： OAI收割

来源：地理科学与资源研究所

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