Abstract:

The cloud cover in the optical remote sensing images will obscure the ground information to varying degrees，which causes the blurring and loss of the surface observation information and greatly affects the imaging quality of remote sensing images.Therefore，the detection and evaluation of cloud cover in remote sensing images are the basis and key to further analyzing and utilizing remote sensing image information.Through sufficient investigation and summary，the development trend and representative work of cloud detection methods based on remote sensing images at home and abroad since the 1990s were reviewed.Cloud detection methods based on remote sensing images were divided into three categories：methods based on band threshold，methods based on classical machine learning and methods based on deep learning.Besides，the public datasets at home and abroad used in the related research on cloud detection were summarized，and the accuracy of some representative cloud detection methods was compared.In addition to the standard cloud detection methods，the cloud and fog（haze）detection，cloud and snow detection，cloud shadow detection and cloud removal methods related to cloud detection were also briefly reviewed.Based on the review and summary of cloud detection work above，the existing problems and future development trends of cloud detection were analyzed and prospected.

Key words:

remote sensing image, cloud detection, band threshold, machine learning, deep learning, survey