| Abstract： |
Cloud coverage hinders the effective range of Earth observation by optical remote sensing satellites. Rapid and accurate cloud detection is an important step in the product generation process of remote sensing applications. Given the lack of suitable and high-quality cloud detection models in the Google Earth Engine cloud platform, this study takes tropical cloudy Sri Lanka as the study area and constructs a Sentinel-2 image cloud detection model coupled with support vector machines and Cloud-Score algorithm. Through experiments, the cloud detection accuracy of this method was compared to the QA60 method, Cloud-Score algorithm, and Function of mask (Fmask) from the point of view of visual interpretation and quantitative analysis. Compared with the other three cloud detection methods, the cloud detection model proposed in this study has the highest overall accuracy, reaching 98.21%, with an extremely low omission and commission errors. The model can accurately identify the cloud boundary and meet the cloud detection pre-processing requirements of Sentinel-2 remote sensing products. |
