Incident Angle Beam Research Articles

C-Band Simulations of Melting Icebergs Using GRECOSAR and an EM Model: Varying Wind Conditions at Lower Beam Mode

This article presents an electromagnetic backscatter model of iceberg and compares the modeled scattering behavior with C-band RADARSAT-2 synthetic aperture radar (SAR) images. It also explores iceberg SAR signature variability over various ocean parameters. Three-dimensional (3-D) profiles of icebergs were captured in a field study off the coast of Bonavista, NL, Canada, in June 2017 at the time of an SAR satellite overpass. The 3-D profiles were captured from a vessel, using a LiDAR and multibeam sonar. The SAR image and 3-D profiles were captured within hours of one another. Simulated SAR images of the icebergs were generated in a simulator called GRECOSAR with the satellite, target orientation, and ocean parameters that closely mimic the real SAR scene. A new ocean model was introduced to model an ocean backscatter at satellites' lower incidence angle beam mode. Comparison between real and simulated SAR images of the icebergs shows good agreement in terms of SAR signature, total radar cross section, and polarimetric decomposition. Wind direction was varied over 90° extent to observe icebergs' backscatter variability in the simulator. Furthermore, simulated SAR images were generated for low and high wind conditions. Our study finds that the macrostructure of the melt iceberg dominates its polarimetric behavior of its backscatter. Large variability of iceberg SAR signature over varying ocean parameters was also observed. A mathematical model that considers the melting condition of iceberg suggested that significant backscattering can reflect from top surface when the melt water layer could be as little as 0.1 mm.

Hyperspectral imaging-based credit card verifier structure with adaptive learning

We propose and experimentally demonstrate a hyperspectral imaging-based optical structure for verifying a credit card. Our key idea comes from the fact that the fine detail of the embossed hologram stamped on the credit card is hard to duplicate, and therefore its key color features can be used for distinguishing between the real and counterfeit ones. As the embossed hologram is a diffractive optical element, we shine a number of broadband light sources one at a time, each at a different incident angle, on the embossed hologram of the credit card in such a way that different color spectra per incident angle beam are diffracted and separated in space. In this way, the center of mass of the histogram on each color plane is investigated by using a feed-forward backpropagation neural-network configuration. Our experimental demonstration using two off-the-shelf broadband white light emitting diodes, one digital camera, and a three-layer neural network can effectively identify 38 genuine and 109 counterfeit credit cards with false rejection rates of 5.26% and 0.92%, respectively. Key features include low cost, simplicity, no moving parts, no need of an additional decoding key, and adaptive learning.