BSG-WSL: BackScatter-guided weakly supervised learning for water mapping in SAR images
Extracting and analyzing water resources in Synthetic Aperture Radar (SAR) images is crucial for flood management and environmental resource planning due to the ability to monitor ground all-weather and all-time. However, extracting water entirely from high-resolution SAR images in diverse scenarios...
Saved in:
Main Authors: | Kai Wang, Zhongle Ren, Biao Hou, Weibin Li, Licheng Jiao |
---|---|
Format: | Article |
Language: | English |
Published: |
Elsevier
2025-02-01
|
Series: | International Journal of Applied Earth Observations and Geoinformation |
Subjects: | |
Online Access: | http://www.sciencedirect.com/science/article/pii/S1569843225000329 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Water-Matching CAM: A Novel Class Activation Map for Weakly-Supervised Semantic Segmentation of Water in SAR Images
by: Kai Wang, et al.
Published: (2025-01-01) -
Temporal backscatter characterisation of ratoon rice crops based on Sentinel-1 intensity data
by: Vidya Nahdhiyatul Fikriyah, et al.
Published: (2025-12-01) -
Enhanced BP Algorithm Combined With Semantic Segmentation and Subaperture for Improving Agricultural Scene Image Quality in GEO SAR
by: Yifan Wu, et al.
Published: (2025-01-01) -
SSL-MBC: Self-Supervised Learning With Multibranch Consistency for Few-Shot PolSAR Image Classification
by: Wenmei Li, et al.
Published: (2025-01-01) -
A Testing Framework for Joint Communication and Sensing in Synthetic Aperture Radars
by: Alex Piccioni, et al.
Published: (2025-01-01)