A Machine-Learning-Based Ocean-Current Velocity Inversion Model Using OCN From Sentinel-1 Observations
High-precision ocean-current velocity inversion is crucial for maritime activities. Synthetic aperture radar (SAR) has become a key data source for ocean-current velocity inversion. However, traditional methods, such as the Doppler centroid anomaly (DCA) and along-track interferometry methods, face...
Saved in:
| Main Authors: | Yang Bai, Yubin Zhang, Xudong Zhang, Xiaofeng Li |
|---|---|
| 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/10938213/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Direct Ocean Tidal Current Measurements From Space: Enhanced Interpretation of Sentinel‐1 Doppler Shift Signals
by: M. G. Hart‐Davis, et al.
Published: (2025-07-01) -
An investigation of inversion method to measure the radial velocity of Kuroshio from Sentinel-1 SAR data
by: Benhua Tan, et al.
Published: (2024-12-01) -
Surface Current Observations in the Southeastern Tropical Indian Ocean Using Drifters
by: Prescilla Siji, et al.
Published: (2025-04-01) -
Ocean Currents Velocity Hindcast and Forecast Bias Correction Using a Deep-Learning Approach
by: Ali Muhamed Ali, et al.
Published: (2024-09-01) -
Sensitivity Analysis of P-Wave Polarization Direction and Velocity Gradient Inversion
by: Jingru Zhao, et al.
Published: (2025-01-01)