Hierarchical Transfer Learning with Transformers to Improve Semantic Segmentation in Remote Sensing Land Use
文献类型：期刊论文
| 作者 | Chen, Miaomiao1,2; Li, Lianfa1,2 |
| 刊名 | REMOTE SENSING
 |
| 出版日期 | 2025 |
| 卷号 | 17期号:2页码:290 |
| 关键词 | land use semantic segmentation transformer hierarchical transfer learning |
| DOI | 10.3390/rs17020290 |
| 产权排序 | 1 |
| 文献子类 | Article |
| 英文摘要 | Land use classification remains a significant challenge in remote sensing semantic segmentation. While convolutional neural networks (CNNs) are widely used, their inherent limitations, such as restricted receptive fields, hinder their widespread application in remote sensing. Additionally, the scarcity of labeled remote sensing data and domain shift issues adversely impact deep learning model performance. This study proposes a hierarchical transfer learning framework for fine-category semantic segmentation tasks, leveraging the powerful global relationship modeling capabilities of Transformer models to classify land use in Dongpo District, Meishan City, in mainland China. Our framework represents multilevel transfer learning, progressing from non-remote sensing classification to coarse classification, then to the refined classification of remote sensing. We compared the performance of Transformer models with representative baseline CNNs like U-Net and DeepLab V3+. Results show that the Swin-Unet model outperforms the other models used in this study. It achieved the highest test mean intersection over union (MIoU) of 0.837 and 0.810 for residential and transportation in level 1 (coarse) classification, respectively, and 0.545 for irrigated land in level 2 (fine-grained) classification. Transfer learning from pre-trained models significantly enhanced semantic segmentation accuracy compared to random parameter initialization (ranging from 0.4% to 17.7%), with up to a 17.7% improvement in test MIoU for the public land category. The hierarchical transfer learning framework further improved segmentation accuracy for corresponding level 2 categories, leveraging pre-trained level 1 models. Our study shows the applicability of Transformer-based transfer learning in remote sensing land use classification. |
| URL标识 | 查看原文 |
| WOS研究方向 | Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology |
| 语种 | 英语 |
| WOS记录号 | WOS:001404693200001 |
| 出版者 | MDPI |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/211348]  |
| 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 | Li, Lianfa |
| 作者单位 | 1.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Datun Rd, Beijing 100101, Peoples R China; 2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China |
| 推荐引用方式 GB/T 7714 | Chen, Miaomiao,Li, Lianfa. Hierarchical Transfer Learning with Transformers to Improve Semantic Segmentation in Remote Sensing Land Use[J]. REMOTE SENSING,2025,17(2):290. |
| APA | Chen, Miaomiao,&Li, Lianfa.(2025).Hierarchical Transfer Learning with Transformers to Improve Semantic Segmentation in Remote Sensing Land Use.REMOTE SENSING,17(2),290. |
| MLA | Chen, Miaomiao,et al."Hierarchical Transfer Learning with Transformers to Improve Semantic Segmentation in Remote Sensing Land Use".REMOTE SENSING 17.2(2025):290. |
入库方式： OAI收割
来源：地理科学与资源研究所
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