Assessing the Usefulness of Iceberg Electromagnetic Backscatter Modeling Using a C-Band SAR Classifier

Abstract

This letter presents the validation of an electromagnetic (EM) backscatter model of icebergs at C-band by comparing the performances of target classifiers trained with both modeled and real synthetic aperture radar (SAR) data. Simulated SAR data were obtained in a combination of imaging beam modes and scene parameters to produce 216 simulated Sentinel-1 C-band SAR images. Parameters consisted of Sentinel-1 IW1 (33.1°) and IW3 (43.1°) beam modes with varying wind speed (5 and 10 m/s), wind direction (0°, 45°, and 90°), and target orientation (0°, 45°, and 90°). Simulations were created from an EM SAR simulator called GRECOSAR, which took 3-D profiles of iceberg and ship targets and parameters necessary to closely mimic the real scenes. 3-D models of three icebergs were captured in a field study off the coast of Bonavista, Newfoundland, and Labrador, Canada in June 2017. Three generic ship models were sourced from an online inventory and scaled to a size equivalent to that of the iceberg targets. Real SAR image data were drawn from in-house data set collected from a complementary research program. Classifiers including support vector machine (SVM), Random Forest (RanFor), k-nearest neighbor (kNN), and neural network (NN) were trained with targets from modeled SAR data and then gradually mixed with real SAR data. Target classifier performance from the modeled target data was shown to be similar to classifiers trained entirely from real SAR data. The similarity in accuracy provides an indication of the validity of the modeled SAR data for this specific application.