3 papers
eess.SP2026
Beyond Backscatter: InSAR coherence from detected SAR images
Francescopaolo Sica, Andrea Pulella, Michael Schmitt
In this work, we propose a deep learning framework for coherence regression directly from detected SAR images, without the need for accurate coregistration. A Residual U-Net is tra…
cs.CV2026
Prompting Foundation Models for Zero-Shot Ship Instance Segmentation in SAR Imagery
Islam Mansour, Francescopaolo Sica, Michael Schmitt
Synthetic Aperture Radar (SAR) plays a critical role in maritime surveillance, yet deep learning for SAR analysis is limited by the lack of pixel-level annotations. This paper expl…
cs.CV2026
Location Is All You Need: Continuous Spatiotemporal Neural Representations of Earth Observation Data
Mojgan Madadikhaljan, Jonathan Prexl, Isabelle Wittmann +2
In this work, we present LIANet (Location Is All You Need Network), a coordinate-based neural representation that models multi-temporal spaceborne Earth observation (EO) data for a…