Deep learning-based automated terrain classification using high-resolution DEM data
文献类型：期刊论文
| 作者 | Yang, Jiaqi2; Xu, Jun; Lv, Yunshuo3; Zhou, Chenghu2; Zhu, Yunqiang1; Cheng, Weiming2 |
| 刊名 | INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION
 |
| 出版日期 | 2023-04-01 |
| 卷号 | 118页码:103249 |
| 关键词 | Landform classification Semantic segmentation Fully convolutional network Residual network |
| ISSN号 | 1569-8432 |
| DOI | 10.1016/j.jag.2023.103249 |
| 文献子类 | Article |
| 英文摘要 | Landforms are a fundamental component of the natural environment, and digital terrain mapping on a large spatial scale is important when studying landforms. In this study, we adopted a semantic segmentation model in computer vision to classify elementary landform types using AW3D30 digital elevation model (DEM) data. We built a semantic segmentation model with an FCN-ResNet architecture that extracts features using a residual network (ResNet) and obtains pixel-level segmentation of the DEM using a fully convolutional network (FCN). A lightweight decoder based on skip connections was adopted to maintain detailed information at different scales. We used the 1:1,000,000 Chinese landform map as the label and tested different combinations of terrain factors. The experiments indicate that increasing the terrain factors has no significant influence on the model, and the semantic information can be learned using only DEM data. The model has strong feature extraction capability and is tolerant to noise and error. The results of landform category prediction confirm that deep learning methods have strong potential for landform classification and will have great application prospects in the field of geomorphological research. |
| WOS关键词 | LANDFORM CLASSIFICATION ; SEGMENTATION ; TOPOGRAPHY ; FEATURES |
| WOS研究方向 | Remote Sensing |
| WOS记录号 | WOS:000951494900001 |
| 出版者 | ELSEVIER |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/190498]  |
| 专题 | 资源与环境信息系统国家重点实验室_外文论文 |
| 作者单位 | 1.Liaoning Normal Univ, Sch Geog, Dalian 116029, Peoples R China 2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China 3.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100049, Peoples R China 4.Jiangsu Ctr Collaborat Innovat Geog Informat Resou, Nanjing 210023, Peoples R China |
| 推荐引用方式 GB/T 7714 | Yang, Jiaqi,Xu, Jun,Lv, Yunshuo,et al. Deep learning-based automated terrain classification using high-resolution DEM data[J]. INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION,2023,118:103249. |
| APA | Yang, Jiaqi,Xu, Jun,Lv, Yunshuo,Zhou, Chenghu,Zhu, Yunqiang,&Cheng, Weiming.(2023).Deep learning-based automated terrain classification using high-resolution DEM data.INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION,118,103249. |
| MLA | Yang, Jiaqi,et al."Deep learning-based automated terrain classification using high-resolution DEM data".INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION 118(2023):103249. |
入库方式： OAI收割
来源：地理科学与资源研究所
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