A lightweight Deeplab V3+ network integrating deep transitive transfer learning and attention mechanism for burned area identification
Abstract Complete and accurate burned area map data are needed to document spatial and temporal patterns of fires, to quantify their drivers, and to assess the impacts on human and natural systems. To achieve the the purpose of identifying burned area accurately and efficiency from remote sensing im...
Saved in:
| Main Authors: | Lizhi Liu, Ying Guo, Erxue Chen, Zengyuan Li, Yu Li, Yang Liu, Qiang Zhang, Bing Wang |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-05-01
|
| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-024-66060-7 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Multi-Feature Lightweight DeeplabV3+ Network for Polarimetric SAR Image Classification with Attention Mechanism
by: Junfei Shi, et al.
Published: (2025-04-01) -
A lightweight semantic segmentation method for concrete bridge surface diseases based on improved DeeplabV3+
by: Zhiyuan Yu, et al.
Published: (2025-03-01) -
Steel surface defect segmentation with SME-DeeplabV3+
by: Haiyan Zhang, et al.
Published: (2025-01-01) -
Insights of semantic segmentation using the DeepLab architecture for autonomous driving
by: Javed Subhedar, et al.
Published: (2025-06-01) -
Damage Detection and Segmentation in Disaster Environments Using Combined YOLO and Deeplab
by: So-Hyeon Jo, et al.
Published: (2024-11-01)