Caps-SSENet: An Improved Estimation Method for SAR Ship Size

Accurate estimation of the sizes of ship targets plays a critical role in the task of ship classification in synthetic aperture radar (SAR) images. Existing deep neural networks (DNNs)-based methods for SAR ship size estimation (SSE) often adopt a fully connected structure that has limited capability in accurately modeling the relationships of features extracted from SAR images, leading to degraded performance of size estimation. It has been demonstrated that capsule networks provide new guidelines to capture relationships of image features by replacing traditional neurons with capsules, where the dynamic routing strategy is used to calculate correlations among capsules. In this letter, we propose an improved method for SAR SSE based on the capsule network named Caps-SSE network (SSENet). In our Caps-SSENet, a capsule-neural-mixing size mapping module is designed to transform the extracted image features into capsules and complete the estimation of ship sizes using informative feature correlations from dynamic routing. In addition, an average scaled mean square error (ASMSE) loss is proposed to improve the size estimation performance of small ships. Experimental results based on measured SAR data show that the proposed method reduces the estimation error of ship sizes in SAR images in comparison with the existing state-of-the-art method.