SEMPNet: enhancing few-shot remote sensing image semantic segmentation through the integration of the segment anything model
Few-shot semantic segmentation has attracted increasing attention due to its potential for low dependence on annotated samples. While extensively explored in the computer vision community, these techniques are primarily designed for natural images, resulting in limited generalization to remote sensi...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2024-12-01
|
| Series: | GIScience & Remote Sensing |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/15481603.2024.2426589 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|