4 papers
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…
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…
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…
SenPa-MAE: Sensor Parameter Aware Masked Autoencoder for Multi-Satellite Self-Supervised Pretraining
Jonathan Prexl, Michael Schmitt
This paper introduces SenPa-MAE, a transformer architecture that encodes the sensor parameters of an observed multispectral signal into the image embeddings. SenPa-MAE can be pre-t…