Self-Normalizing Generative Adversarial Network for Super-Resolution Reconstruction of SAR Images

Abstract

High-resolution images with abundant detailed information are necessary elements for various applications of synthetic aperture radar (SAR). In this paper, a novel super-resolution image reconstruction method based on self-normalizing generative adversarial network (SNGAN) is proposed. Compared with other published GAN-based super-resolution algorithms, the proposed method reflects its superiority in two aspects. First, the scaled exponential linear units (SeLU) is introduced as the activation function of generator to give the GAN system self-normalization ability and make it more suitable for SAR images. Second, the batch normalization layers after convolution are canceled to reduce the computational requirement and model oscillation. Experiment results on the images of TerraSAR and MSTAR dataset demonstrate that the proposed method acquires satisfactory performance on the resolution enhancement and target recognition of SAR images.