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CV-MotionNet: Complex-Valued Convolutional Neural Network for SAR Moving Ship Targets Classification

In the synthetic aperture radar (SAR) images, moving ship targets are defocused due to the movement, which leads to the problem of poor classification accuracy. Therefore, this paper proposes an amplitude-phase-type complex-valued convolutional neural network (AP-CV-CNN) architecture called CV-MotionNet to classify SAR moving ship targets without motion compensation. It utilizes both amplitude and phase information of complex SAR images. CV-MotionNet uses amplitude-phase-type activation function to processing amplitude and phase information more conducive. Then, the proposed CV-MotionNet is tested on simulated five-types SAR moving ship target classification task and GF-3 SAR ship classification. Simulation and experiment show that the classification error can be further reduced if using CV-MotionNet instead of real-valued CNN (RV-CNN) with the same degree of freedom.