TY - JOUR
T1 - Cloud and cloud shadow detection for optical satellite imagery: Features, algorithms, validation, and prospects
AU - Li, Zhiwei
AU - Shen, Huanfeng
AU - Weng, Qihao
AU - Zhang, Yuzhuo
AU - Dou, Peng
AU - Zhang, Liangpei
N1 - Funding Information:
This research was supported by the National Key Research and Development Program of China (No. 2018YFA0605500), the National Natural Science Foundation of China (No. 42101357), the China Postdoctoral Science Foundation (Nos. 2020TQ0229, 2021M692462), and the Fundamental Research Funds for the Central Universities (No. 2042021KF0078). The authors of this review would like to thank the editors and the anonymous reviewers for providing valuable comments, which helped to greatly improve the manuscript.
Publisher Copyright:
© 2022 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS)
PY - 2022/6
Y1 - 2022/6
N2 - 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).
AB - 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).
KW - Cloud detection
KW - Cloud shadow detection
KW - Cloudy and rainy regions
KW - Optical remote sensing
KW - Satellite imagery
UR - http://www.scopus.com/inward/record.url?scp=85129351407&partnerID=8YFLogxK
U2 - 10.1016/j.isprsjprs.2022.03.020
DO - 10.1016/j.isprsjprs.2022.03.020
M3 - Review article
AN - SCOPUS:85129351407
SN - 0924-2716
VL - 188
SP - 89
EP - 108
JO - ISPRS Journal of Photogrammetry and Remote Sensing
JF - ISPRS Journal of Photogrammetry and Remote Sensing
ER -