Are Cloud Detection U-Nets Robust Against in-Orbit Image Acquisition Conditions?

Cloud detection is one of the most important image pre-processing steps that can be performed on-board satellites. It may allow us to reduce the amount of data to analyze or downlink by pruning the cloudy areas, or to make the satellites more autonomous through data-driven image acquisition re-scheduling of the areas obscured by clouds. Thus, building the cloud detection algorithms that can be ultimately deployed in orbit became an important research avenue. In this paper, we investigate the robustness of the fully-convolutional neural networks for cloud detection against the atmospheric conditions that resemble real acquisition settings of the Intuition-1 mission. Our experiments, performed over the original and simulated Landsat-8 images, with the latter reflecting target conditions, shed more light on the performance of deep models and showed how can we verify their robustness in Earth observation tasks for which real images do not exist yet.