Cloud segmentation is a vital task in applications that utilize satellite imagery. A common obstacle in using deep learning-based methods for this task is the insufficient number of images with their annotated ground truths. This work presents a content-aware unpaired image-to-image translation algorithm. It generates synthetic images with different land cover types from original images while preserving the locations and the intensity values of the cloud pixels. Therefore, no manual annotation of ground truth in these images is required. The visual and numerical evaluations of the generated images by the proposed method prove that their quality is better than that of competitive algorithms.

Index Terms— Cloud detection, Landsat 8, remote sensing
imagery, unpaired image-to-image translation