Cloud Cover Assessment in Satellite Images Via Deep Ordinal Classification

The percentage of cloud cover is one of the key indices for satellite data products. To date, cloud cover assessment is performed manually in most groundstations. To facilitate the process, this paper proposes a deep learning approach for cloud cover assessment in quicklook satellite images. The quicklook images from Centre for Remote Imaging, Sensing and Processing (CRISP) are used for demonstration. Same as the manual operation, given a quicklook image, the algorithm returns 8 labels ranging from A to E and *, indicating the cloud percentages in different areas of the image. This is achieved by constructing 8 improved VGG-16 models, where parameters such as the loss function, learning rate and dropout are tailored for better performance. Results indicate that approach is promising, as around 85% of sub-scenes are correctly labelled, and the accuracy is even higher if one ordinal error is accepted. This paper demonstrates a new application in remote sensing using state-of-the-art deep learning techniques.