Classification-based and rule-based methods for cloud detection in high resolution satellite imagery

Cloud cover ratio in electro-optical satellite images is a critical factor for the usability of the images. First step to assess the usability of satellite images is cloud detection for the estimation of a cloud coverage from the analyses and/or removing the clouds from images. Satellite imagery providers supply these cloud ratio and maps for their satellite imagery. In this work, classification-based and rule-based methods are compared for cloud detection in high-resolution satellite imagery. In particular, test images consist of different scenarios such as regular, stereo and cloudy imagery. From the experiments, it has been observed that deep learning-based methods could achieve significant cloud detection performances.