Recognition of three-dimensional rotating ship target for sar images based on complex-valued convolutional neural network

Ship targets have complicated motions such as random swings that change with the waves, which makes the target defocused and azimuth blurred in Synthetic Aperture Radar (SAR) images, and the classification accuracy of three-dimensional rotating ship targets is low. This paper proposes a mixed-type complex-valued convolutional neural network (Mix-CV-CNN). Mix-CV-CNN can make full use of the amplitude and phase information of complex SAR images, and can better complete the classification of SAR three-dimensional rotating ship targets without refocusing the target. Through SAR three-dimensional rotating ship target simulation analysis and actual measurement data verification, the proposed complex-valued convolutional neural network is experimentally analyzed. The superiority of the network improves the accuracy and reliability of SAR three-dimensional rotating ship target classification.