Vectorization Method for Remote Sensing Object Segmentation Based on Frame Field Learning: A Case Study of Greenhouses
文献类型：期刊论文
| 作者 | Yao, Ling2,3; Lu, Yuxiang1,3; Liu, Tang3; Jiang, Hou3; Zhou, Chenghu3 |
| 刊名 | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
 |
| 出版日期 | 2024-06-01 |
| 卷号 | 62页码:5625414 |
| 关键词 | Greenhouses Vectors Remote sensing Image segmentation Image edge detection Feature extraction Activate contour model deep learning frame field learning greenhouse |
| DOI | 10.1109/TGRS.2024.3403425 |
| 产权排序 | 1 |
| 文献子类 | Article |
| 英文摘要 | Deep learning technologies have significantly advanced object information extraction from remote sensing data in recent years, achieving broad application across various industrial sectors. However, information loss exists between remote sensing object raster segmentation and geographic information vector mapping, making it challenging to directly apply raster extraction results to vector mapping. This study, taking the automatic extraction of greenhouses based on remote sensing imagery as an example, proposes a vectorization method for remote sensing object segmentation based on frame field. This method bridges the gap between the object pixel segmentation process and the mask vectorization process through the frame field information outputted by the network, resulting in smoother and more regular vector extraction results. To validate the effectiveness of our framework, we introduce the first high-precision greenhouse vector boundary dataset. Extensive experiments demonstrate that our method significantly mitigates the information loss issue prevalent in traditional vectorization processes, achieving a 5.05% improvement in intersection over union (IoU), a 6.06% increase in recall, and a 5.54% reduction in maximum angular error (MAE) compared to simple vectorization schemes. It outputs more regular greenhouse vector plots, where the precision of the frame field plays a crucial role in the final vectorization quality. This research offers a unique and practical solution, converting remote sensing object segmentation into vector maps. |
| WOS关键词 | SATELLITE |
| WOS研究方向 | Geochemistry & Geophysics ; Engineering ; Remote Sensing ; Imaging Science & Photographic Technology |
| WOS记录号 | WOS:001237717100024 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/205403]  |
| 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 | Yao, Ling |
| 作者单位 | 1.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 101408, Peoples R China 2.Jiangsu Ctr Collaborat Innovat Geog Informat Resou, Nanjing 210023, Peoples R China 3.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China |
| 推荐引用方式 GB/T 7714 | Yao, Ling,Lu, Yuxiang,Liu, Tang,et al. Vectorization Method for Remote Sensing Object Segmentation Based on Frame Field Learning: A Case Study of Greenhouses[J]. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,2024,62:5625414. |
| APA | Yao, Ling,Lu, Yuxiang,Liu, Tang,Jiang, Hou,&Zhou, Chenghu.(2024).Vectorization Method for Remote Sensing Object Segmentation Based on Frame Field Learning: A Case Study of Greenhouses.IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,62,5625414. |
| MLA | Yao, Ling,et al."Vectorization Method for Remote Sensing Object Segmentation Based on Frame Field Learning: A Case Study of Greenhouses".IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 62(2024):5625414. |
入库方式： OAI收割
来源：地理科学与资源研究所
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