UNIVERSITY OF LEEDS, SCHOOL OF EARTH AND ENVIRONMENT (GB)
The Earth’s subaerial volcanoes pose a variety of threats to humanity, yet the vast majority remain unmonitored. However, with the advent of the latest synthetic aperture radar (SAR) satellites, interferometric SAR (InSAR) has evolved into a tool that can be used to monitor the majority of these volcanoes. Whilst challenges such as the automatic and timely creation of interferograms have been addressed, further developments are required to construct a comprehensive monitoring algorithm, that is able to automate the interpretation of these data. This project will seek to develop a deep learning based model that is able to monitor the majority of the world’s subaerial volcanoes using satellite based measurements. This algorithm will incorporate a model that is trained solely on time series of SAR data, and so does not require pre-training on databases of natural images (e.g. ImageNet). Additionally, the model will feature complementary and diverse inputs, such as phase, coherence, and amplitude.