Automatic ship detection in SAR Image based on Multi-scale Faster R-CNN

Abstract

Automatic ship detection of synthetic aperture radar (SAR) images has been widely used in maritime surveillance. SAR images have the characteristics of all-weather, all-day detection. Therefore, many object detection methods ranging from traditional to deep learning techniques have been proposed. However, the objects in large-scale remote sensing images are relatively small, and objects are often appeared at different scales. What’s more, the current ship detection methods are insensitive to small-scale vessels. To solve these problems, a novel multi-scale ship detection method based on a Multi-scale Faster R-CNN network in SAR images is proposed in this paper. Firstly, a multi-scale network is used to decompose the SAR images into a pyramid structure and extract the features. Then, the region proposal network (RPN) is performed using the feature map for each layer to get the proposals that contains ship targets. Finally, these proposals are fed to the classification and regression network to obtain the final detection results. Multi-scale Faster R-CNN achieves the mean average precision(mAP) score 0.986 on the dataset of SAR-Ship-Dataset, which indicates that the proposed method has high detection accuracy and low missing rate.