CRIA: An Enhancement Method For CV-CNN Based on Cross-Fusion of Complex Information of Real and Imaginary Activations

In recent years, the complex-valued convolutional neural network (CV-CNN) for processing complex data has made great use in the field of SAR data processing. In this paper, a complex-valued activation enhancement method named CRIA is constructed based on the cross-fusion of real and imaginary activation in the activation layer of CV-CNN, the core of which is to cross-combine the real and imaginary parts of the activation output of the two activation functions to enhance the overall processing of complex data, to enhance the ability of the network to parse complex value information. By conducting classification experiments on ship slices in SAR images of complex data, the experimental results show that the CRIA method in the activation layer can accelerate the network convergence speed and enhance the network classification performance.