X-Band/C-Band-Comparison of Ship Wake Detectability

Abstract

This study presents an extension to recent ship wake detectability models based on SAR image analysis with machine learning. In contrast to our previous works, we model the detectability of certain wake components individually. The underlying data set is obtained by extracting possible ship wake signatures from SAR imagery by collocation with Automatic Identification System data. The developed detectability models are based on machine learning. They generally confirm previous findings based on simulated SAR data or qualitative image analysis. The results from our previous wake detectability model are compared to initial results from our new wake component detectability model.