The PhD project aims to advance the detection and characterisation of icebergs in heterogeneous background conditions i.e. drifting within regions of open water or embedded within sea ice. Both scenarios present different detection challenges. To this end polarimetric and single-pass interferometric SAR images represent the most advanced data type available from imaging space-borne radar systems. The ambitious, but achievable, goal of this project is (i) the testing and improving of already existing algorithms [3,4] and (ii) the development of novel and robust methods rooted in detection theory, pattern recognition, and machine learning specifically adapted to the targets in question. The application of polarimetric and single-pass interferometric satellite data for iceberg characterization and detection have the greatest potential for success over other data where sea ice conditions are highly complex (e.g., various ice types of different age, topography and thickness), where dense iceberg field exist or where there is a strong contrast between water and ice. Accurate detection and tracking of icebergs are of high impact for the safety of marine traffic and operations, and scientific applications such as studying the interactions between atmosphere, sea ice/icebergs, and ocean. The research aims to address the following points:
1) To characterize icebergs in open and ice-infested waters using polarimetric synthetic aperture radar
The project plans to investigate the application of the polarimetric SAR data for a characteristic analysis of icebergs from the surrounding sea ice or open water under different challenging environmental conditions. The polarimetric characteristics of icebergs and different sea ice types will also be compared with less rich polarimetric datasets, to evaluate the advantages brought by polarimetry. The PhD candidate will evaluate which polarimetric feature or scattering model can do a better job in modelling the appearance of icebergs in images.
2) To detect icebergs in open and ice-infested waters using polarimetric synthetic aperture radar
The use of conventional constant false alarm rate (CFAR) detector has been of great use over the years for detection of marine targets over open water. However, to detect smaller icebergs embedded in sea ice we need more powerful methodologies as the one adopted in [3,4]. The background of SAR images can vary greatly depending on the target they observe, (e.g. different sea ice types and open water). The project aims to use deep convolutional neural networks (CNNs) to improve the detection capabilities. The PhD student will investigate the potential of different polarimetric features with respect to iceberg discrimination from the surrounding sea ice or open water and then use this information for detection of icebergs.
3) To map and estimate icebergs topography and volume using single-pass interferometric SAR
For stationary (grounded) icebergs in fast sea ice, repeat pass monostatic mode is acceptable provided there is no motion of the iceberg between passes. However, for drifting iceberg, bistatic interferometry is required to eliminate iceberg motion effects. The PhD student will address the potential of bistatic single-pass InSAR observations  for retrieving topography and volume of icebergs. The results of InSAR-derived iceberg height and volume will be cross-validated using other source of information such as NASA’s ICESat-2 and Operation IceBridge (OIB). The comparison between ICESat-2, TanDEM-X, and OIB for iceberg height is one major objective of this project.