A Comparison of Numerically Modelled Iceberg Backscatter Signatures with Sentinel-1 C-Band Synthetic Aperture Radar Acquisitions

Abstract

Use of machine learning to develop algorithms for distinguishing iceberg and vessel targets requires large validated data sets that are often costly, time consuming and, in some cases, inaccessible. Generating electromagnetic (EM) backscatter models of iceberg and ship targets can be a vital step in developing a robust iceberg/ship classification algorithm. In this work, EM backscatter models for icebergs are developed using an EM backscatter modelling tool called GRECOSAR and compared with ground truth data. The imaging scene consists of iceberg targets surrounded by the ocean surface. The 3D computer aided design models of the icebergs were obtained using LiDAR and multi-beam sonar data collected during a field program off the coast of Salvage, Newfoundland and Labrador, Canada. While profiling the iceberg targets, a synthetic aperture radar (SAR) image from Sentinel-1A was captured and compared with the simulated SAR images. Comparisons made in terms of total radar cross section (TRCS) and the SAR signature of the targets generally indicate credible simulations. Simulated SAR images were generated at low and high dielectric conditions to mimic cold and melt iceberg surfaces. Variability of the TRCS and morphology as a function of target orientation highlights the usefulness of EM modelling in developing robust iceberg/ship classifiers.