Refocusing for Three-Dimensional Rotating Ship Targets in SAR Images Based on Minimum Entropy Criteria and Generative Adversarial Network
In synthetic aperture radar(SAR)system,the three-dimensional rotation of ship targets in the presence of a medium and high sea state would lead to time-varying Doppler spectrum and image defocusing,which will adversely affect the subsequent information interpretation of ship targets in SAR images.Aiming at the refocusing problem of three-dimen-sional rotating ship targets,this paper proposes a SAR refocusing method for three-dimensional rotating ship target based on minimum entropy criterion and generative adversarial network,and designs the network structure of generator and discrimi-nator.The generator transforms the defocused complex SAR ship image into range-Doppler domain,and estimates the phase error coefficient by range unit using phase error coefficient estimation network,and realizes the compensation of multi-order phase errors.The discriminator is composed of a complex-valued convolutional neural network,and all its ele-ments,including convolution layer,activation function,feature mapping and parameters,are extended to the complex do-main.The minimum entropy criterion and adversarial loss are introduced into the loss function to achieve unsupervised training and avoid the problem that it is difficult to obtain the target labeling samples of non-cooperative ships.Experiments on simulated data and Gaofen-3 data show that the proposed method achieves significant improvements in both refocusing accuracy and efficiency.
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维普
