Land-use classification based on high-resolution remote sensing imagery and deep learning models
文献类型：期刊论文
| 作者 | Hao, Mengmeng2,3; Dong, Xiaohan2,3; Jiang, Dong2,3; Yu, Xianwen1; Ding, Fangyu2,3; Zhuo, Jun2,3 |
| 刊名 | PLOS ONE
 |
| 出版日期 | 2024-04-18 |
| 卷号 | 19期号:4页码:e0300473 |
| DOI | 10.1371/journal.pone.0300473 |
| 产权排序 | 1 |
| 文献子类 | Article |
| 英文摘要 | High-resolution imagery and deep learning models have gained increasing importance in land-use mapping. In recent years, several new deep learning network modeling methods have surfaced. However, there has been a lack of a clear understanding of the performance of these models. In this study, we applied four well-established and robust deep learning models (FCN-8s, SegNet, U-Net, and Swin-UNet) to an open benchmark high-resolution remote sensing dataset to compare their performance in land-use mapping. The results indicate that FCN-8s, SegNet, U-Net, and Swin-UNet achieved overall accuracies of 80.73%, 89.86%, 91.90%, and 96.01%, respectively, on the test set. Furthermore, we assessed the generalization ability of these models using two measures: intersection of union and F1 score, which highlight Swin-UNet's superior robustness compared to the other three models. In summary, our study provides a systematic analysis of the classification differences among these four deep learning models through experiments. It serves as a valuable reference for selecting models in future research, particularly in scenarios such as land-use mapping, urban functional area recognition, and natural resource management. |
| WOS关键词 | SUPPORT VECTOR MACHINES ; ACCURACY ASSESSMENT ; COVER CHANGE ; SEGMENTATION ; AREA |
| WOS研究方向 | Science & Technology - Other Topics |
| WOS记录号 | WOS:001207320100123 |
| 出版者 | PUBLIC LIBRARY SCIENCE |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/205399]  |
| 专题 | 资源利用与环境修复重点实验室_外文论文 |
| 通讯作者 | Ding, Fangyu; Zhuo, Jun |
| 作者单位 | 1.ByteDance Inc, Beijing, Peoples R China 2.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing, Peoples R China 3.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing, Peoples R China |
| 推荐引用方式 GB/T 7714 | Hao, Mengmeng,Dong, Xiaohan,Jiang, Dong,et al. Land-use classification based on high-resolution remote sensing imagery and deep learning models[J]. PLOS ONE,2024,19(4):e0300473. |
| APA | Hao, Mengmeng,Dong, Xiaohan,Jiang, Dong,Yu, Xianwen,Ding, Fangyu,&Zhuo, Jun.(2024).Land-use classification based on high-resolution remote sensing imagery and deep learning models.PLOS ONE,19(4),e0300473. |
| MLA | Hao, Mengmeng,et al."Land-use classification based on high-resolution remote sensing imagery and deep learning models".PLOS ONE 19.4(2024):e0300473. |
入库方式： OAI收割
来源：地理科学与资源研究所
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