Enhancing Cross-Domain Remote Sensing Scene Classification by Multi-Source Subdomain Distribution Alignment Network
Multi-source domain adaptation (MSDA) in remote sensing (RS) scene classification has recently gained significant attention in the visual recognition community. It leverages multiple well-labeled source domains to train a model capable of achieving strong generalization on the target domain with lit...
Saved in:
| Main Authors: | Yong Wang, Zhehao Shu, Yinzhi Feng, Rui Liu, Qiusheng Cao, Danping Li, Lei Wang |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-04-01
|
| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/17/7/1302 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Spatial-Topological-Semantic alignment for cross domain scene classification of remote sensing images with few source labels
by: Binquan Li, et al.
Published: (2024-12-01) -
Domain Knowledge Decomposition for Cross-Domain Few-Shot Scene Classification From Remote Sensing Imagery
by: Can Li, et al.
Published: (2025-01-01) -
Low Saturation Confidence Distribution-based Test-Time Adaptation for cross-domain remote sensing image classification
by: Yu Liang, et al.
Published: (2025-05-01) -
Domain-Incremental Learning Paradigm for scene understanding via Pseudo-Replay Generation
by: Zhifeng Xie, et al.
Published: (2025-09-01) -
Dynamic convolutional model based on distribution-collaboration strategy for remote sensing scene classification
by: Chenjun Xu, et al.
Published: (2025-08-01)