
Dr Odysseas Pappas
MEng, PhD
Current positions
Honorary Senior Research Associate
School of Computer Science
Contact
Press and media
Many of our academics speak to the media as experts in their field of research. If you are a journalist, please contact the University’s Media and PR Team:
Research interests
My main research interests revolve around signal processing for remote sensing and geospatial applications, with a particular focus on SAR processing. More specifically these include inverse imaging problems, compressive sampling, machine learning and generative AI methods for inverse problems, object/target detection, superpixel segmentation and classification, statistical modelling, anomaly detection and multimodal image fusion.
My current research focuses on the use of diffusion models for compressive SAR imaging, as well as on ship and ship wake detection and classification in SAR images for maritime monitoring applications. Prior projects have included work on InSAR for volcano deformation monitoring, river segmentation, mapping, and analysis from SAR imagery, as well as applications of compressive sampling to biomedical imaging modalities.
Projects and supervisions
Thesis supervisions
Publications
Recent publications
02/04/2026NASTaR: A NovaSAR-Based Automated Ship Target Recognition Dataset
IEEE Geoscience and Remote Sensing Letters
Deep Unfolded Approximate Message Passing for Quantitative Acoustic Microscopy Image Reconstruction: 2025 International Conference on Acoustics, Speech, and Signal Processing
- International Conference on Acoustics, Speech, and Signal Processing (ICASSP)
ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Diffusion probabilistic models for compressive SAR imaging: SPIE Defense + Commercial Sensing 2025 - Proceedings of SPIE
Proceedings of SPIE
Measuring topographic change after volcanic eruptions using multistatic SAR satellites: Simulations in preparation for ESA’s Harmony mission
Remote Sensing of Environment
R-Sparse R-CNN: SAR Ship Detection Based on Background-Aware Sparse Learnable Proposals
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing



