Open SAR Topic
Are you passionate about shaping the future of remote sensing?
With the increasing number of radar satellite missions from NASA-India’s NISAR to the European Space Agency’s BIOMASS, Sentinel1 and several upcoming platforms, the focus of Earth observation is expanding beyond optical sensors. Synthetic Aperture Radar (SAR) is now a key technology, offering continuous day-and-night monitoring with minimal impact from clouds or atmospheric conditions.
In line with this growing momentum, this project offers an open-ended opportunity to explore the potential of SAR and machine learning across a wide range of applications like soil moisture mapping , land cover classification, forest and urban height estimation, infrastructure and road-quality monitoring (e.g., roughness detection using high-resolution SAR), advanced polarimetric-interferometric (PolInSAR), or tomographic (TomoSAR) techniques for building stability analysis, 3D subsurface imaging, such as snowpack and depth characterization or underground heterogeneity mapping for identifying potential underground water resources. etc.
You will gain hands-on experience with SAR and PolSAR data, learn about polarization concepts, and apply modern machine learning methods to uncover new insights and applications from radar images. Join to explore how the next generation of radar satellites will redefine the future of remote sensing.
The methodology focuses on understanding the capabilities of SAR images and exploring standard physical or model-based processing frameworks ( PolSAR, PolInSAR, TomoSAR, RVOG,...) for selected applications. The use of machine learning methods is encouraged, while you will be learning optimal strategies for preparing and feeding SAR data into machine learning models, as well as adapting these models to the unique characteristics of SAR images.
https://people.utwente.nl/h.aghababaei