A Multi-Branch Attention Fusion Method for Semantic Segmentation of Remote Sensing Images
In recent years, advancements in remote sensing image observation technology have significantly enriched the surface feature information captured in remote sensing images, posing greater challenges for semantic information extraction from remote sensing imagery. While convolutional neural networks (...
Saved in:
| Main Authors: | Kaibo Li, Zhenping Qiang, Hong Lin, Xiaorui Wang |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-05-01
|
| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/17/11/1898 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Multi-Source Remote Sensing Images Semantic Segmentation Based on Differential Feature Attention Fusion
by: Di Zhang, et al.
Published: (2024-12-01) -
Mixed multi-branch feature fusion model for efficient automatic building extraction from high-resolution remote sensing images
by: Yaohui Liu, et al.
Published: (2025-07-01) -
Dynamic atrous attention and dual branch context fusion for cross scale Building segmentation in high resolution remote sensing imagery
by: Yaohui Liu, et al.
Published: (2025-08-01) -
A dynamic attention mechanism for road extraction from high-resolution remote sensing imagery using feature fusion
by: Haoming Bai, et al.
Published: (2025-05-01) -
AFNE-Net: Semantic Segmentation of Remote Sensing Images via Attention-Based Feature Fusion and Neighborhood Feature Enhancement
by: Ke Li, et al.
Published: (2025-07-01)