Cloud and cloud shadow detection for optical satellite imagery: Features, algorithms, validation, and prospects

The presence of clouds prevents optical satellite imaging systems from obtaining useful Earth observation information and negatively affects the processing and application of optical satellite images. Therefore, the detection of clouds and their accompanying shadows is an essential step in preprocessing optical satellite images and has emerged as a popular research topic in recent decades due to the interest in image time series analysis and remote sensing data mining. This review first analyzes the trends of the field, summarizes the progress and achievements in the cloud and cloud shadow detection methods in terms of features, algorithms, and validation of results, and then discusses existing problems, and provides our prospects at the end. We aim at identifying the emerging research trends and opportunities, while providing guidance for selecting the most suitable methods for coping with cloud contaminated problems faced by optical satellite images, an extremely important issue for remote sensing of cloudy and rainy areas. In the future, expected improvements in accuracy and generalizability, the combination of physical models and deep learning, as well as artificial intelligence and online big data processing platforms will be able to further promote processing efficiency and facilitate applications of image time series. In addition, this review collects the latest open-source tools and datasets for cloud and cloud shadow detection and launches an online project (Open Satellite Image Cloud Detection Resources, i.e., OpenSICDR) to share the latest research outputs (https://github.com/dr-lizhiwei/OpenSICDR).