Using convolutional neural network (CNN) approach for ship detection in Sentinel-1 SAR imagery

Abstract

Accurate maritime ship surveillance and monitoring ensures compliance with port regulations and standards. The growing volume of waterborne traffic however, has made this goal difficult to achieve in applications like maritime traffic control, ship search and rescue, territorial regulation, and fishery management. Detection of ships is complicated, especially under unfavourable conditions, such as during night-time or on cloudy days. Synthetic aperture radar (SAR) provides high-resolution data that can overcome these limitations. Using machine-learning techniques to detect ships in a SAR based image can increase the accuracy of identification detection results as compared to traditional image-based object detection methods. Sentinel-1 SAR images from 2015 to 2018 were used in this exploratory study presenting an analysis of effective ship detection and ship count in a congested sea environment using a Convolutional Neural Networks (CNNs) method, the Faster R-CNN VGG16. Experiments using sixteen convolutional layers in the model yielded promising and significant detection, identification and ship count results for the port of Shanghai.