SAR Ship Classification based on Second-order Synchrosqueezing Transform and HOG Feature

Accurate classification and identification of vessels in remote sensing satellite imagery is critical for ocean monitoring and resource management. The ability to extract information from remote-sensing data is of paramount importance. To exploit the non-stationary characteristics of synthetic aperture radar (SAR) target, a comprehensive SAR ship recognition framework is designed by combing the second-order synchrosqueezing transform (SST), an effective non-stationary signal processing tool, with the histogram of oriented gradient (HOG) feature in this paper. Firstly, the second-order SST is performed on SAR images to describe the non-stationary characteristics of ships at different times and frequencies. Secondly, HOG features are utilized to effectively extract the non-stationary information of SAR ships and provide more discriminative input for the deep learning network. Then, the optimal ResNet model is selected as the convolutional neural network (CNN) classifier to automatically fuse the non-stationary features and abstract features of SAR ships. Experiments on two open SAR ship datasets (OpenSARShip and FUSAR-Ship) show that the proposed method achieves accurate classification and outperforms the state-of-the-art (SOTA) CNN-based methods in terms of robustness and generalization ability. The positive effect of non-stationary characteristics on SAR ship classification is verified.