Complex Landscape Rice Extraction Using Integrated Sentinel-2 Spectral-Temporal-Spatial Imagery and a Hybrid Deep Learning Architecture
文献类型：期刊论文
| 作者 | Liu, Tianjiao2; Duan, Si-Bo2; Liu, Niantang2; Zhang, Youzhi1; Chen, Jiankui3; Zhang, Li4; Li, Dong5 |
| 刊名 | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
 |
| 出版日期 | 2025 |
| 卷号 | 63页码:21 |
| 关键词 | Feature extraction Crops Accuracy Remote sensing Transformers Data mining Training Long short term memory Data models Vegetation mapping Active learning hybrid deep learning model rice extraction Sentinel-2 spectral-temporal-spatial imagery |
| ISSN号 | 0196-2892 |
| DOI | 10.1109/TGRS.2025.3580712 |
| 通讯作者 | Duan, Si-Bo(duansibo@caas.cn) |
| 英文摘要 | Rice extraction in complex landscapes is a challenging issue in remote sensing, particularly in areas with diverse land-use types and spatiotemporal variability. To enhance the accuracy of rice extraction, this study proposes a novel approach integrating Sentinel-2 spectral-temporal-spatial imagery with a hybrid deep learning architecture for extracting single-cropping and double-cropping rice. First, a time-series dataset of spectral and texture features was constructed to capture the seasonal variations of rice. Second, an active learning strategy was employed to select high-confidence samples, and spectral, temporal, and spatial information was integrated into a unified dataset. Finally, a hybrid deep learning model, convolutional-Transformer hybrid network (CTH-Net), was developed, combining convolutional neural networks (CNNs) and Transformer networks. The model incorporates a fusion module to effectively integrate multiscale temporal, spatial, and spectral features and a residual module to improve gradient flow, mitigating the vanishing gradient problem in deep networks. Results demonstrate that the CTH-Net achieved 99.69% overall accuracy in rice extraction, maintaining >96% accuracy for single-cropping rice, double-cropping rice, and abandoned land. It outperformed models like CNNs, Transformers, long short-term memory (LSTM), and support vector machines (SVMs) in handling fragmented rice distributions and mixed land types, significantly improving extraction accuracy. This study provides an efficient and reliable solution for rice extraction in complex landscapes, supporting agricultural monitoring and management. |
| WOS关键词 | TIME-SERIES ; CLASSIFICATION ; EMISSIONS ; MACHINE ; SYSTEMS |
| WOS研究方向 | Geochemistry & Geophysics ; Engineering ; Remote Sensing ; Imaging Science & Photographic Technology |
| 语种 | 英语 |
| WOS记录号 | WOS:001521530000016 |
| 资助机构 | National Key Research and Development Program of China ; Hebei Provincial Natural Science Foundation of China ; Langfang Science and Technology Research and Development Program of China |
| 源URL | [http://ir.yic.ac.cn/handle/133337/41259]  |
| 专题 | 烟台海岸带研究所_海岸带信息集成与综合管理实验室 |
| 通讯作者 | Duan, Si-Bo |
| 作者单位 | 1.Inst Agr Remote Sensing & Informat, Heilongjiang Acad Agr Sci, Harbin 150086, Peoples R China 2.Chinese Acad Agr Sci, Inst Agr Resources & Reg Planning, State Key Lab Efficient Utilizat Arable Land China, Beijing 100081, Peoples R China 3.Hebei Oriental Univ, Sch Artificial Intelligence, Langfang 065001, Peoples R China 4.Guizhou Univ, Coll Big Data & Informat Engn, Guiyang 550025, Peoples R China 5.Chinese Acad Sci, Yantai Inst Coastal Zone Res, Yantai 264003, Peoples R China |
| 推荐引用方式 GB/T 7714 | Liu, Tianjiao,Duan, Si-Bo,Liu, Niantang,et al. Complex Landscape Rice Extraction Using Integrated Sentinel-2 Spectral-Temporal-Spatial Imagery and a Hybrid Deep Learning Architecture[J]. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,2025,63:21. |
| APA | Liu, Tianjiao.,Duan, Si-Bo.,Liu, Niantang.,Zhang, Youzhi.,Chen, Jiankui.,...&Li, Dong.(2025).Complex Landscape Rice Extraction Using Integrated Sentinel-2 Spectral-Temporal-Spatial Imagery and a Hybrid Deep Learning Architecture.IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,63,21. |
| MLA | Liu, Tianjiao,et al."Complex Landscape Rice Extraction Using Integrated Sentinel-2 Spectral-Temporal-Spatial Imagery and a Hybrid Deep Learning Architecture".IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 63(2025):21. |
入库方式： OAI收割
来源：烟台海岸带研究所
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