Fusing EO and LiDAR for SAR Image Translation with Multi-Modal Generative Adversarial Networks

To generate high-quality Synthetic Aperture Radar (SAR) images, the translation of Electro-optical (EO) to SAR images has been studied in recent years. However, these methods cannot make use of other modal information, such as Light Detection and Ranging (LiDAR), to enhance their performance, due to the absence of well-registered multi-modal remote sensing data. Therefore, in this paper, we first construct an organized high-resolution real-world benchmark dataset, which comprises 479 registered multi-modal images. We then explore how to adapt the state-of-the-art method, named the query-selected attention model (QS-Attn), to make it more suitable for integrating EO and LiDAR images into real SAR image translation. Extensive experiments have been conducted to evaluate our proposed method and compare it with state-of-the-art methods. The experimental results validate the superiority and effectiveness of our approach.