.png){kind=icon}
返回
Winter wheat mapping using unbalanced multi-source remote sensing data
.png){kind=icon}
OA
AI
DOI：10.1080/17538947.2025.2509814.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
The integration of optical and synthetic aperture radar (SAR) remote sensing images enhances the acquisition of winter wheat planting information and improves the accuracy of winter wheat mapping. However, the distinct imaging mechanisms of multi-source sensors present challenges, particularly when optical remote sensing data suffers from missing information due to atmospheric conditions. This data imbalance can lead to biased optimization during joint learning processes, hindering the network’s ability to capture comprehensive spatio-temporal information on winter wheat. To address this issue, this study introduces a multi-source spatio-temporal feature joint balanced optimization model (MS-JBM). The model enhances winter wheat mapping accuracy by extracting complete spatio-temporal feature information through balanced extraction and complementary two-phase spatio-temporal features. MS-JBM initially leverages similar and stable temporal information from multiple sources to obtain an unbiased representation of multi-source spatio-temporal features. Subsequently, it utilizes complete SAR spatio-temporal features as the primary foundation for constructing winter wheat growth knowledge, guiding the comprehensive interaction of multi-source spatio-temporal features. This process yields complete spatio-temporal feature information of winter wheat, with semantic consistency across multiple interaction rounds ensuring process stability. Comprehensive evaluations on three datasets demonstrate that the proposed MS-JBM model surpasses existing multi-source spatio-temporal feature extraction models.
Keyword:
Winter wheat extraction
multi-source data
deep learning
imbalanced learning
期刊
IF:
4.9
论文数:
2.0K
被引数:
4.7K
机构
引用论文
Song, Shanshan, Shuqing Zhang, and Xiang Wu. 2024. “Multi-view Graph Convolutional Networks Based on Multi-source Information Fusion for Mechanical Fault Diagnosis.” Structural Health Monitoring: 14759217241276019. https://doi.org/10.1177/14759217241276019 (Open in a new window)Web of Science ®(Open in a new window)Google Scholar基于多源信息融合的多视图图卷积网络用于机械故障诊断.png)
IF0
PREAI
分享收藏Wu, Zhenghong, Hongkai Jiang, Xin Wang, and Hongxuan Zhu. 2023. “Knowledge Correlation Graph-Guided Multisource Interaction Domain Adaptation Network for Rotating Machinery Fault Diagnosis.” ISA Transactions 142:663–682. https://doi.org/10.1016/j.isatra.2023.07.036 (Open in a new window)PubMed(Open in a new window)Google Scholar吴正宏，江宏凯，王鑫，朱宏瑄。2023。“知识关联图引导的多源交互域自适应网络在旋转机械故障诊断中的应用。”《仪表技术与传感器》142：663–682。https://doi.org/10.1016/j.isatra.2023.07.036 (在新窗口中打开)PubMed(在新窗口中打开)谷歌学术
IF0
PREAI
分享收藏Wu, Wei, Yapeng Liu, Kun Li, Haiping Yang, Liao Yang, and Zuohui Chen. 2025. “Stfcropnet: A Spatio-Temporal Fusion Network for Crop Classification in Multi-resolution Remote Sensing Images.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 18: 4736–4750. https://doi.org/10.1109/JSTARS.2025.3531886 (Open in a new window)Google Scholar吴伟，刘亚鹏，李坤，杨海平，杨燎，陈作辉。2025。“多分辨率遥感图像作物分类的时空融合网络STFCropNet”。《IEEE地球观测与遥感应用精选主题杂志》18：4736–4750。https://doi.org/10.1109/JSTARS.2025.3531886（在新窗口中打开）谷歌学术
IF0
PREAI
分享收藏Zhao, Wenzhi, Yang Qu, Liqiang Zhang, and Kaiyuan Li. 2022b. “Spatial-aware sar-Optical Time-Series Deep Integration for Crop Phenology Tracking.” Remote Sensing of Environment 276:113046. https://doi.org/10.1016/j.rse.2022.113046 (Open in a new window)Web of Science ®(Open in a new window)Google Scholar赵文智, 杨区, 张立强, 李开元. 2022b. “空间感知SAR-光学时序深度融合用于作物物候监测.” 环境遥感 276:113046. https://doi.org/10.1016/j.rse.2022.113046 (在新窗口打开)Web of Science ®(在新窗口打开)谷歌学术
IF0
PREAI
分享收藏Dong, Wei, Junsheng Wu, Xinwan Zhang, Zongwen Bai, Peng Wang, and Marcin Woźniak. 2022. “Improving Performance and Efficiency of Graph Neural Networks by Injective Aggregation.” Knowledge-Based Systems 254:109616. https://doi.org/10.1016/j.knosys.2022.109616 (Open in a new window)Web of Science ®(Open in a new window)Google Scholar董伟，吴俊生，张新 Wan，白宗文，王鹏，Marcin Woźniak. 2022. “通过注入聚合提高图神经网络的性能和效率。” 知识-Based 系统 254:109616. https://doi.org/10.1016/j.knosys.2022.109616 (在新窗口中打开)Web of Science ®(在新窗口中打开)谷歌学术
IF0
PREAI
分享收藏Ienco, Dino, Roberto Interdonato, Raffaele Gaetano, and Dinh Ho Tong Minh. 2019. “Combining Sentinel-1 and Sentinel-2 Satellite Image Time Series for Land Cover Mapping via a Multi-source Deep Learning Architecture.” ISPRS Journal of Photogrammetry and Remote Sensing 158:11–22. https://doi.org/10.1016/j.isprsjprs.2019.09.016 (Open in a new window)Web of Science ®(Open in a new window)Google ScholarIenco, Dino, Roberto Interdonato, Raffaele Gaetano, and Dinh Ho Tong Minh. 2019. “结合哨兵-1和哨兵-2卫星影像时间序列进行土地覆盖制图的多源深度学习架构。” 《摄影测量与遥感学报》 158:11–22. https://doi.org/10.1016/j.isprsjprs.2019.09.016 (在新窗口中打开)Web of Science ®(在新窗口中打开)谷歌学术
IF0
PREAI
分享收藏Cai, Wenting, Shuhe Zhao, Yamei Wang, Fanchen Peng, Joon Heo, and Zheng Duan. 2019. “Estimation of Winter Wheat Residue Coverage Using Optical and sar Remote Sensing Images.” Remote Sensing 11 (10): 1163. https://doi.org/10.3390/rs11101163 (Open in a new window)Web of Science ®(Open in a new window)Google Scholar蔡文婷，赵舒和，王雅梅，彭凡晨，崔俊，段镇。2019。《利用光学和SAR遥感影像估算冬小麦秸秆覆盖率》。《遥感》11（10）：1163。https://doi.org/10.3390/rs11101163（在新窗口打开）Web of Science ®（在新窗口打开）谷歌学术
IF0
PREAI
分享收藏Chen, Peipei, Yue Li, Xiaojun Liu, Yongchao Tian, Yan Zhu, Weixing Cao, and Qiang Cao. 2023. “Improving Yield Prediction Based on Spatio-Temporal Deep Learning Approaches for Winter Wheat: A Case Study in Jiangsu Province, China.” Computers and Electronics in Agriculture 213:108201. https://doi.org/10.1016/j.compag.2023.108201 (Open in a new window)Web of Science ®(Open in a new window)Google Scholar陈培培, 李跃, 刘晓君, 田永超, 朱燕, 曹卫行, 曹强. 2023. “基于时空深度学习方法的冬小麦产量预测改进：以中国江苏省为例.” 农业计算机与电子 213:108201. https://doi.org/10.1016/j.compag.2023.108201 (在新窗口打开)Web of Science ®(在新窗口打开)谷歌学术
IF0
PREAI
分享收藏Dong, Jie, Zhouye Pang, Yangyang Fu, Qiongyan Peng, Xiangqian Li, and Wenping Yuan. 2024. "Annual Winter Wheat Mapping Dataset in China from 2001 to 2020.” Scientific Data 11 (1218): 1–13. https://doi.org/10.1038/s41597-024-04065-7 (Open in a new window)PubMed(Open in a new window)Google Scholar董杰，庞卓野，付洋洋，彭琼燕，李湘茜，袁文平. 2024. "2001年至2020年中国冬小麦年度分布数据集." 科学数据 11 (1218): 1–13. https://doi.org/10.1038/s41597-024-04065-7
IF0
PREAI
分享收藏Hashemi, Mahya GZ, Ehsan Jalilvand, Hamed Alemohammad, Pang-Ning Tan, and Narendra N Das. 2024. “Review of Synthetic Aperture Radar with Deep Learning in Agricultural Applications.” ISPRS Journal of Photogrammetry and Remote Sensing 218:20–49. https://doi.org/10.1016/j.isprsjprs.2024.08.018 (Open in a new window)Web of Science ®(Open in a new window)Google Scholar哈希米，玛雅 GZ，埃桑·贾利尔瓦德，哈米德·阿莱姆霍马德，庞宁·坦，纳伦德拉·纳特·达斯. 2024. “深度学习在农业应用中合成孔径雷达的综述.” 摄影测量与遥感国际摄影测量与遥感学会会刊 218:20–49. https://doi.org/10.1016/j.isprsjprs.2024.08.018 (在新窗口中打开)Web of Science ®(在新窗口中打开)谷歌学术
IF0
PREAI
分享收藏
查看更多内容