OPTICAL FLOW FOR GLACIER MOTION ESTIMATION

12 citations as recorded by crossref.

1. Performance Testing of Optical Flow Time Series Analyses Based on a Fast, High-Alpine Landslide D. Hermle et al. 10.3390/rs14030455
2. Image georectification and feature tracking toolbox: ImGRAFT A. Messerli & A. Grinsted 10.5194/gi-4-23-2015
3. Time-lapse optical flow regularization for geophysical complex phenomena monitoring H. Hadhri et al. 10.1016/j.isprsjprs.2019.02.007
4. A Bayesian ice thickness estimation model for large-scale applications M. Werder et al. 10.1017/jog.2019.93
5. Measuring landslide-driven ground displacements with high-resolution surface models and optical flow E. Carle et al. 10.1016/j.cageo.2023.105378
6. Weekly Glacier Flow Estimation from Dense Satellite Time Series Using Adapted Optical Flow Technology B. Altena & A. Kääb 10.3389/feart.2017.00053
7. RivQNet: Deep Learning Based River Discharge Estimation Using Close‐Range Water Surface Imagery S. Ansari et al. 10.1029/2021WR031841
8. Evaluating Image Tracking Approaches for Surface Velocimetry With Thermal Tracers D. Lin et al. 10.1029/2018WR024507
9. Global Circulation of Latent Heat in the Earth’s Atmosphere According to Data from Satellite Radiothermovision D. Ermakov 10.1134/S000143381809013X
10. Estimating Landslide Surface Displacement by Combining Low-Cost UAV Setup, Topographic Visualization and Computer Vision Techniques V. Yordanov et al. 10.3390/drones7020085
11. Adaptation and Evaluation of an Optical Flow Method Applied to Coregistration of Forest Remote Sensing Images G. Brigot et al. 10.1109/JSTARS.2016.2578362
12. How accurate are estimates of glacier ice thickness? Results from ITMIX, the Ice Thickness Models Intercomparison eXperiment D. Farinotti et al. 10.5194/tc-11-949-2017