Super-Resolution Rural Road Extraction from Sentinel-2 Imagery Using a Spatial Relationship-Informed Network
文献类型：期刊论文
| 作者 | Jia, Yuanxin3; Zhang, Xining2,4; Xiang, Ru2,4; Ge, Yong1,2,4,5 |
| 刊名 | REMOTE SENSING
 |
| 出版日期 | 2023-09-01 |
| 卷号 | 15期号:17页码:18 |
| 关键词 | spatial relationship deep learning super-resolution mapping rural road extraction feature enhancement |
| DOI | 10.3390/rs15174193 |
| 通讯作者 | Ge, Yong(gey@lreis.ac.cn) |
| 英文摘要 | With the development of agricultural and rural modernization, the informatization of rural roads has been an inevitable requirement for promoting rural revitalization. To date, however, the vast majority of road extraction methods mainly focus on urban areas and rely on very high-resolution satellite or aerial images, whose costs are not yet affordable for large-scale rural areas. Therefore, a deep learning (DL)-based super-resolution mapping (SRM) method has been considered to relieve this dilemma by using freely available Sentinel-2 imagery. However, few DL-based SRM methods are suitable due to these methods only relying on the spectral features derived from remote sensing images, which is insufficient for the complex rural road extraction task. To solve this problem, this paper proposes a spatial relationship-informed super-resolution mapping network (SRSNet) for extracting roads in rural areas which aims to generate 2.5 m fine-scale rural road maps from 10 m Sentinel-2 images. Based on the common sense that rural roads often lead to rural settlements, the method adopts a feature enhancement module to enhance the capture of road features by incorporating the relative position relation between roads and rural settlements into the model. Experimental results show that the SRSNet can effectively extract road information, with significantly better results for elongated rural roads. The intersection over union (IoU) of the mapping results is 68.9%, which is 4.7% higher than that of the method without fusing settlement features. The extracted roads show more details in the areas with strong spatial relationships between the settlements and roads. |
| WOS关键词 | PIXEL ; ALGORITHM |
| 资助项目 | We would like to thank Copernicus Open Access Hub for providing the Sentinel 2 MSI images and the Google Brain for providing the Tensorflow2.1 framework. |
| WOS研究方向 | Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology |
| 语种 | 英语 |
| WOS记录号 | WOS:001060683200001 |
| 出版者 | MDPI |
| 资助机构 | We would like to thank Copernicus Open Access Hub for providing the Sentinel 2 MSI images and the Google Brain for providing the Tensorflow2.1 framework. |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/196826]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Ge, Yong |
| 作者单位 | 1.Southern Marine Sci & Engn Guangdong Lab Zhuhai, Zhuhai 519082, Peoples R China 2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 3.Natl Forestry & Grassland Adm, Acad Forest Inventory & Planning, Beijing 100714, Peoples R China 4.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China 5.Changan Univ, Sch Land Engn, Xian 710064, Peoples R China |
| 推荐引用方式 GB/T 7714 | Jia, Yuanxin,Zhang, Xining,Xiang, Ru,et al. Super-Resolution Rural Road Extraction from Sentinel-2 Imagery Using a Spatial Relationship-Informed Network[J]. REMOTE SENSING,2023,15(17):18. |
| APA | Jia, Yuanxin,Zhang, Xining,Xiang, Ru,&Ge, Yong.(2023).Super-Resolution Rural Road Extraction from Sentinel-2 Imagery Using a Spatial Relationship-Informed Network.REMOTE SENSING,15(17),18. |
| MLA | Jia, Yuanxin,et al."Super-Resolution Rural Road Extraction from Sentinel-2 Imagery Using a Spatial Relationship-Informed Network".REMOTE SENSING 15.17(2023):18. |
入库方式： OAI收割
来源：地理科学与资源研究所
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