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A dual-branch spatio-temporal Transformer for enhancing cross-regional transferability of winter wheat extraction using small training datasets

文献类型：期刊论文

作者He, Chenyang1,2; Song, Jia2,3
刊名INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION
出版日期2025-09-01
卷号143页码:104785
关键词Winter wheat Deep learning Transformer Small training dataset Time series
ISSN号1569-8432
DOI10.1016/j.jag.2025.104785
产权排序1
文献子类Article
英文摘要Accurate identification of winter wheat from remote sensing imagery is crucial for large-scale agricultural monitoring. Despite the success of Transformer-based deep learning models in various fields, their application in crop identification has been limited by the scarcity of extensive labeled training data. This study proposes a dualbranch spatio-temporal Transformer (DST-Transformer) for winter wheat extraction from Sentinel-2 imagery using a small training dataset. By independently extracting temporal and spatial features, the DST-Transformer effectively delineates crop boundaries and reduces misclassification. Experiments demonstrate its effectiveness with small training datasets, achieving over 90% overall accuracy (OA) and 88.25% mean intersection over union (MIoU) when evaluating on test datasets. The DST-Transformer was further applied to large-scale winter wheat extraction across Shandong Province, China (an area 66 times larger than the training region) to evaluate its cross-regional transferability. Evaluation results showed OA over 92% and MIoU exceeding 85% at all validation sites, highlighting the DST-Transformer's robustness and strong generalization capability. This study underscores the DST-Transformer's potential for large-scale crop identification and illustrates the promise of Transformer-based architectures for efficient, high-precision crop mapping with small training datasets, advancing the application of deep learning in agricultural remote sensing.
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WOS关键词TIME-SERIES ; IMAGERY
WOS研究方向Remote Sensing
语种英语
WOS记录号WOS:001574917200004
出版者ELSEVIER
源URL[http://ir.igsnrr.ac.cn/handle/311030/216121]  
专题资源与环境信息系统国家重点实验室_外文论文
通讯作者Song, Jia
作者单位1.Univ Chinese Acad Sci, Beijing 100049, Peoples R China;
2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China;
3.Jiangsu Ctr Collaborat Innovat Geog Informat Resou, Nanjing 210023, Peoples R China
推荐引用方式
GB/T 7714		 He, Chenyang,Song, Jia. A dual-branch spatio-temporal Transformer for enhancing cross-regional transferability of winter wheat extraction using small training datasets[J]. INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION,2025,143:104785.
APA He, Chenyang,&Song, Jia.(2025).A dual-branch spatio-temporal Transformer for enhancing cross-regional transferability of winter wheat extraction using small training datasets.INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION,143,104785.
MLA He, Chenyang,et al."A dual-branch spatio-temporal Transformer for enhancing cross-regional transferability of winter wheat extraction using small training datasets".INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION 143(2025):104785.

入库方式： OAI收割

来源：地理科学与资源研究所

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