Name
Abstract | ||
|---|---|---|
Urban villages (UVs) are distinctive products formed in the process of rapid urbanization. The fine-grained mapping of UVs from satellite images has always been a considerable challenge because of the complex urban structures and the insufficiency of labeled samples. In this letter, we propose using the domain adaptation strategy to tackle the domain shift problem by employing adversarial learning to tune the semantic segmentation network so as to adaptively obtain similar outputs for input images from different domains. The proposed method was coupled with several segmentation networks, including U-Net, RefineNet, and DeepLab v3+, and the results show that domain adaptation can significantly improve the pixel-level mapping of UVs. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/LGRS.2019.2947473 | IEEE Geoscience and Remote Sensing Letters |
Keywords | DocType | Volume |
Image segmentation,Semantics,Training,Feature extraction,Adaptation models,Deep learning,Satellites | Journal | 17 |
Issue | ISSN | Citations |
8 | 1545-598X | 1 |
PageRank | References | Authors |
0.35 | 0 | 7 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Qian Shi | 1 | 83 | 13.37 |
| Qian Shi | 2 | 83 | 13.37 |
| Mengxi Liu | 3 | 2 | 2.74 |
| Xiaoping Liu | 4 | 510 | 37.78 |
| Pengyuan Zhang | 5 | 50 | 19.46 |
| Jinxing Yang | 6 | 1 | 0.35 |
| Xia Li | 7 | 47 | 3.72 |