SAR-to-Optical Image Translation and Cloud Removal Based on Conditional Generative Adversarial Networks: Literature Survey, Taxonomy, Evaluation Indicators, Limits and Future Directions
Quan Xiong, Guoqing Li, Xiaochuang Yao, Xiaodong Zhang
Abstract
Due to the limitation of optical images that their waves cannot penetrate clouds, such images always suffer from cloud contamination, which causes missing information and limitations for subsequent agricultural applications, among others. Synthetic aperture radar (SAR) is able to provide surface information for all times and all weather. Therefore, translating SAR or fusing SAR and optical images to obtain cloud-free optical-like images are ideal ways to solve the cloud contamination issue. In this paper, we investigate the existing literature and provides two kinds of taxonomies, one based on the type of input and the other on the method used. Meanwhile, in this paper, we analyze the advantages and disadvantages while using different data as input. In the last section, we discuss the limitations of these current methods and propose several possible directions for future studies in this field.