GAN with ASPP for SAR Image to Optical Image Conversion

Abstract

Researchers can gain more intuitive information by converting synthetic aperture radar (SAR) images to optical images using generative adversarial networks (GANs). However, their GANs have poor feature extraction ability, which leads to color conversion errors and loss of details. Therefore, to solve this problem, we add an atrous spatial pyramid pooling (ASPP) module to GAN to enhance the feature extraction ability, i.e., ASPP-GAN. ASPP module can extract multi-resolution feature responses, enabling the network to focus on both overall and detailed features for better feature extraction ability. The experimental results on public SEN1-2 datasets show that ASPP-GAN has a significant improvement over the traditional GAN, i.e., Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity (SSIM) values are improved by about 20%.