Deep Self-Supervised Disturbance Mapping With the OPERA Sentinel-1 Radiometric Terrain Corrected SAR Backscatter Product
Mapping land surface disturbances supports disaster response, resource and ecosystem management, and climate adaptation efforts. Synthetic aperture radar (SAR) is an invaluable tool for disturbance mapping, providing consistent time-series images of the ground regardless of weather or illumination c...
Saved in:
| Main Authors: | Harris Hardiman-Mostow, Charles Marshak, Alexander L. Handwerger |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
|
| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10947561/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
BSG-WSL: BackScatter-guided weakly supervised learning for water mapping in SAR images
by: Kai Wang, et al.
Published: (2025-02-01) -
SAR Radiometric Cross-Calibration Based on Multiple Pseudoinvariant Calibration Sites With Extensive Backscattering Coefficient Range
by: Yongsheng Zhou, et al.
Published: (2025-01-01) -
Dataset of Sentinel-1 SAR and Sentinel-2 RGB-NDVI imageryMendeley Data
by: Ahmed Alejandro Cardona-Mesa, et al.
Published: (2024-12-01) -
Restricted Label-Based Self-Supervised Learning Using SAR and Multispectral Imagery for Local Climate Zone Classification
by: Amjad Nawaz, et al.
Published: (2025-04-01) -
Transformer-Based Dual-Branch Spatial–Temporal–Spectral Feature Fusion Network for Paddy Rice Mapping
by: Xinxin Zhang, et al.
Published: (2025-06-01)