A fully convolutional neural network for low-complexity single-stage ship detection in Sentinel-1 SAR images

Abstract

Ship detection is a fundamental task for SAR-based maritime surveillance. Besides providing high reliability, a good detector is required to be computationally light, in order to analyze huge areas in a reasonable time. We propose a fully convolutional neural network for ship detection in SAR images. Thanks to a relatively simple architecture, complexity remains low enough to allow for a single-stage approach, thus avoiding the possible errors of CFAR pre-screening. Experiments on a Sentinel-1 dataset prove the proposed CNN to be much more reliable than CFAR detection.