TKP-Net: A Three Keypoint Detection Network for Ships Using SAR Imagery

Abstract

Remote-sensing ship monitoring is a crucial area of research with key applications in military and civilian fields. The ability to extract information, such as ship length, width, and heading from remote-sensing data, particularly from synthetic aperture radar (SAR) images, is of paramount importance. Current state-of-the-art SAR image ship monitoring focuses primarily on ship detection. Assessing the direction of ships usually relies on the observability of wake features. However, the observability of these wake features is often affected by factors, such as the SAR system parameters, ship attributes, and dynamic marine environments. This can make accurate direction assessments a challenging task. In response to these challenges, this study has presented a novel and effective algorithm for ship monitoring from SAR images based on an anchor-free framework and the powerful feature extraction capabilities of convolutional neural networks. The proposed method learned the scattering and morphological information of a ship's bow and stern from high-resolution SAR images to determine the ship's direction with a high level of accuracy using a rotating bounding box. The algorithm was tested on a dataset, achieving an average precision of 90.8% and bow classification accuracy of 92.5%, demonstrating its potential contributing to the advancement of remote sensing.

Keywords

• Convolutional neural network (CNN)
• keypoint
• ship detection
• ship head classification
• synthetic aperture radar (SAR)