Integration of harvester trajectory and satellite imagery for large-scale winter wheat mapping using deep positive and unlabeled learning
文献类型：期刊论文
| 作者 | Xiong, Xingguo1; Yang, Jie1,2; Zhong, Renhai1; Dong, Jinwei3; Huang, Jingfeng4; Ting, K. C.2,5; Ying, Yibin1,6; Lin, Tao1,6 |
| 刊名 | COMPUTERS AND ELECTRONICS IN AGRICULTURE
 |
| 出版日期 | 2024 |
| 卷号 | 216页码:14 |
| ISSN号 | 0168-1699 |
| 关键词 | Harvester trajectory Satellite images Data fusion Deep learning Crop mapping |
| DOI | 10.1016/j.compag.2023.108487 |
| 通讯作者 | Lin, Tao(lintao1@zju.edu.cn) |
| 英文摘要 | Limited accurate ground truth labels are the primary constraint for data-driven modeling analysis of large-scale crop mapping. Existing labeling methods largely rely on field surveys, visual interpretation, and historical ground information. These laborintensive approaches are often limited by spatiotemporal heterogeneity of crop distribution and encounter the challenge of gathering extensive crop labels. The massive operating trajectories of agricultural machinery contain precise location information of the crop fields, providing a new source for accurate crop labels at a large spatial scale. This study develops a large-scale crop mapping workflow through widespread harvester trajectory and 10 m Sentinel-2 imagery. The trajectory-based automatic labeling method is developed to generate 287,533 winter wheat labels by jointly using harvester coordinates and satellite images. These generated one-class ground labels are further used to develop positive and unlabeled learning based deep learning models for winter wheat mapping. The Positive and Unlabeled Learning-based Convolutional Neural Network (PUL-CNN) outperforms the other four one-class based classifiers with an F1 score of 94.4 % at 12 study sites. The estimated county-level winter wheat acreage agrees well with census data with R2 of 0.86 in the overall study region. The interpretation analysis based on the Shapley Additive Explanation method shows the heading and greening stages are the critical periods for wheat mapping, aligning well with the separability in Normalized Difference Vegetation Index (NDVI) curves. The results of winter wheat mapping demonstrate the integration of harvester trajectory and remote sensing data facilitates large-scale winter wheat mapping. To the best of our knowledge, this is the first study that fuses operating trajectories of agricultural machinery and satellite images for large-scale crop mapping based on the deep positive and unlabeled learning approach. This study could be possibly applied for better understanding the land cover and land use changes. |
| WOS关键词 | TIME-SERIES ; LAND-COVER ; AREA ; CLASSIFICATION ; ACCURACY ; YIELD ; MODEL ; INDEX |
| 资助项目 | National Natural Science Foundation of China[32071894] ; CAS Interdisciplinary Innovation Team[JCTD-2021-04] ; Zhejiang University |
| WOS研究方向 | Agriculture ; Computer Science |
| 语种 | 英语 |
| 出版者 | ELSEVIER SCI LTD |
| WOS记录号 | WOS:001139103700001 |
| 资助机构 | National Natural Science Foundation of China ; CAS Interdisciplinary Innovation Team ; Zhejiang University |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/202068]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Lin, Tao |
| 作者单位 | 1.Zhejiang Univ, Coll Biosyst Engn & Food Sci, Hangzhou 310058, Zhejiang, Peoples R China 2.Zhejiang Univ, ZJU UIUC Inst, Int Campus, Haining 314400, Zhejiang, Peoples R China 3.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Land Surface Pattern & Simulat, Beijing 100101, Peoples R China 4.Zhejiang Univ, Inst Appl Remote Sensing & Informat Technol, Hangzhou 310058, Zhejiang, Peoples R China 5.Univ Illinois, Dept Agr & Biol Engn, Urbana, IL USA 6.Key Lab Intelligent Equipment & Robot Agr Zhejiang, Hangzhou, Peoples R China |
| 推荐引用方式 GB/T 7714 | Xiong, Xingguo,Yang, Jie,Zhong, Renhai,et al. Integration of harvester trajectory and satellite imagery for large-scale winter wheat mapping using deep positive and unlabeled learning[J]. COMPUTERS AND ELECTRONICS IN AGRICULTURE,2024,216:14. |
| APA | Xiong, Xingguo.,Yang, Jie.,Zhong, Renhai.,Dong, Jinwei.,Huang, Jingfeng.,...&Lin, Tao.(2024).Integration of harvester trajectory and satellite imagery for large-scale winter wheat mapping using deep positive and unlabeled learning.COMPUTERS AND ELECTRONICS IN AGRICULTURE,216,14. |
| MLA | Xiong, Xingguo,et al."Integration of harvester trajectory and satellite imagery for large-scale winter wheat mapping using deep positive and unlabeled learning".COMPUTERS AND ELECTRONICS IN AGRICULTURE 216(2024):14. |
入库方式： OAI收割
来源：地理科学与资源研究所
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