SAR-to-Optical Image Translation With Hierarchical Latent Features

Abstract

Due to the all-weather and all-time imaging capability of Synthetic Aperture Radar (SAR), SAR remote sensing analysis has attracted much attention recently. However, compared with optical images, SAR images are more difficult to be interpreted. If a SAR image could be translated into its corresponding optical image, then the generated optical image would be helpful for assisting the interpretation. Addressing this issue, we investigate how to translate SAR images to optical ones in this work, and propose a parallel generative adversarial model for SAR-to-optical image translation, called Parallel-GAN, consisting of a backbone image translation sub-network and an adjoint optical image reconstruction sub-network. Under the proposed model, the backbone image translation sub-network is designed to translate SAR images to optical ones, and simultaneously some of its intermediate layers are required to output similar latent features to those from the corresponding layers of the adjoint image reconstruction sub-network. Thanks to the imposed hierarchical latent optical features, the proposed Parallel-GAN could achieve the SAR-to-optical image translation effectively. Extensive experimental results on three public datasets demonstrate that the proposed method outperforms ten state-of-the-art methods for SAR-to-optical image translation.