LAUSR

Identification of clouds and their shadows are two major preprocessing steps for providing an effective interpretation of remotely sensed optical images. Although deep learning-based methods have proved to deliver competent performance for detecting clouds and cloud shadows, improving their generalization ability for reliable results requires a large number of training images and accurate ground truths. As creating ground truth of cloud and cloud shadow is expensive, a practical way to generate more images and their ground truths is to use data augmentation methods. We propose two new data augmentation approaches (one for generating synthetic clouds in scenes and the other one for creating cloud shadows with different levels of shade) so as to achieve natural-looking images with little or no effort required for creating their ground truths. Our experiments show that while each of these approaches is capable of boosting cloud and cloud shadow segmentation individually, the fusion of them improves detecting cloud and shadow in a simultaneous learning pipeline, leading to outperforming the state-of-the-art results.