Ship Detection From Raw SAR Echoes Using Convolutional Neural Networks

Abstract

Synthetic Aperture Radar (SAR) is an indispensable tool for marine monitoring. Conventional data processing involves data down-linking and on-ground operations for image focusing, analysis and ship detection. These steps take significant amount of time, resulting in potentially critical delays. In this work, we propose a ship detection algorithm that operates directly on raw SAR echoes, based on convolutional neural networks. To evaluate our approach, we performed experiments using raw data simulations and real raw SAR data from Sentinel-1 stripmap mode scenes. Preliminary results on this set show the capability of detecting multiple ships from raw data with similar accuracy as using Single-Look-Complex (SLC) images as input. Simultaneously, running time is reduced significantly, by-passing the image focusing step. This illustrates the great potential of deep learning, moving towards more intelligent SAR systems.