LAUSR

Cloud detection is a fundamental step for optical satellite image applications. Existing deep learning methods can provide more accurate cloud detection results. However, the performance of these methods relies on a large number of label samples, whose collection is time-consuming and high-cost. In addition, cloud detection is challenging in high-brightness scenes due to cloud and high-brightness objects having a similar spectral features. In this study, we propose a cloud index driven spectral-spatial-context attention network (SSCA-net) for cloud detection, which relies on no effort to manually collect label samples and can improve the accuracy of cloud detection in high-brightness scenes. The label samples are automatically generated from the cloud index by using dual-threshold, which is then expanded to improve the completeness of cloud mask labels. We designed SSCA-net with the spectral-spatial-context aware module and spectral-spatial-context information aggregation module, aimed to improve the accuracy of cloud detection in high-brightness scenes. The results show that the proposed SSCA-net achieved good performance with an average overall accuracy of 97.69% and an average kappa coefficient of 92.71% on the Sentinel-2 and Landsat-8 datasets. This article provides fresh insight into how advanced deep attention networks and cloud indexes can be integrated to obtain high accuracy of cloud detection on high-brightness scenes.