A Real-Time Orthophoto Generation Approach for UAV Based on Deep Learning Visual Feature
文献类型：期刊论文
| 作者 | Du, Bing1,2,3; Ye, Huping1,2,3; Zhang, Yuyu1,2,3; Liao, Xiaohan1,2,3 |
| 刊名 | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
 |
| 出版日期 | 2025 |
| 卷号 | 63页码:5640713 |
| 关键词 | Real-time systems Autonomous aerial vehicles Simultaneous localization and mapping Visualization Cameras Feature extraction Accuracy Deep learning Three-dimensional displays Sensors orthophoto real-time remote sensing uncrewed aerial vehicle (UAV) |
| ISSN号 | 0196-2892 |
| DOI | 10.1109/TGRS.2025.3599405 |
| 产权排序 | 1 |
| 文献子类 | Article |
| 英文摘要 | In recent years, there has been a growing demand for uncrewed aerial vehicles (UAVs) to generate real-time orthophotos across various fields, including emergency response, surveying, and land assessment. Deep learning for visual feature extraction has proven highly effective, showing robust performance in complex environments characterized by dynamic scenes, low-texture areas, and significant appearance variations. In this study, we propose a real-time UAV orthophoto generation approach based on deep learning. Our approach uses simultaneous localization and mapping (SLAM) pipeline based on deep features to estimate camera poses and georeferencing. Orthorectified images are seamlessly mosaicked in real-time using optimal orthogonality values and multiband blending algorithms. Experimental results indicate that deep features contribute to accurate orthophoto generation. Compared with current state-of-the-art methods, our approach produces high-quality orthophotos at a reduced computational cost. |
| URL标识 | 查看原文 |
| WOS关键词 | ORTHORECTIFICATION ; ALGORITHM |
| WOS研究方向 | Geochemistry & Geophysics ; Engineering ; Remote Sensing ; Imaging Science & Photographic Technology |
| 语种 | 英语 |
| WOS记录号 | WOS:001570451200032 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/216187]  |
| 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 | Ye, Huping; Liao, Xiaohan |
| 作者单位 | 1.Chinese Acad Sci, State Key Lab Resources & Environm Informat Syst, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China; 2.Civil Aviat Adm China, Key Lab Low Altitude Geog Informat & Air Route, Beijing 100101, Peoples R China; 3.Univ Chinese Acad Sci, Beijing 100049, Peoples R China |
| 推荐引用方式 GB/T 7714 | Du, Bing,Ye, Huping,Zhang, Yuyu,et al. A Real-Time Orthophoto Generation Approach for UAV Based on Deep Learning Visual Feature[J]. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,2025,63:5640713. |
| APA | Du, Bing,Ye, Huping,Zhang, Yuyu,&Liao, Xiaohan.(2025).A Real-Time Orthophoto Generation Approach for UAV Based on Deep Learning Visual Feature.IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,63,5640713. |
| MLA | Du, Bing,et al."A Real-Time Orthophoto Generation Approach for UAV Based on Deep Learning Visual Feature".IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 63(2025):5640713. |
入库方式： OAI收割
来源：地理科学与资源研究所
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