MO4.R11.5
FiLM-GPNet: Geometry-Aware Pseudo-Supervised Phase Restoration with Zero-Shot Generalization for Large Temporal InSAR Stacks
Getnet Demil, University of oulu, Finland; Muhammad Farhan Humayun, Tomi Westerlund, Jukka Heikkonen, University of Turku, Finland; Mourad Oussalah, University of oulu, Finland
Session:
MO4.R11: Data Fusion Contest 2026 Oral
Track:
Theory and Techniques
Location:
Cabinet
Presentation Time:
Monday, 10 August, 17:15 - 17:30
Session Co-Chairs:
Ronny Hänsch, DLR and Scott Staniewicz, Capella Space
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Session MO4.R11
MO4.R11.1: Overview of 2026 Data Fusion Contest
Ronny Hänsch, German Aerospace Center, Germany
MO4.R11.2: TRISAR: Self-Supervised Triplet Metric Learning for Temporal SAR Interpretation
Jamil József Ghazal, Vera Könyves, HUN-REN Institute for Computer Science and Control (SZTAKI), Hungary; Jung András, Faculty of Informatics, Eötvös Loránd University (ELTE), Hungary
MO4.R11.3: T-SAR-JEPA: SELF-SUPERVISED TEMPORAL ANOMALY DETECTION IN SAR AMPLITUDE STACKS VIA LATENT PREDICTION
Kerod Woldesenbet, Independent Researcher, United States; Abem Woldesenbet, Dakota State University, United States
MO4.R11.4: Phase Gradient Voting: Unwrap-Free Deformation Screening for Small-Satellite X-Band InSAR Stacks
Yasuhito Nagase, Josaphat Tetuko Sri Sumantyo, Chiba University, Japan
MO4.R11.5: FiLM-GPNet: Geometry-Aware Pseudo-Supervised Phase Restoration with Zero-Shot Generalization for Large Temporal InSAR Stacks
Getnet Demil, University of oulu, Finland; Muhammad Farhan Humayun, Tomi Westerlund, Jukka Heikkonen, University of Turku, Finland; Mourad Oussalah, University of oulu, Finland
Resources
No resources available.