A Novel Spectral Indices-Driven Spectral-Spatial-Context Attention Network for Automatic Cloud Detection

Abstract

Cloud detection is a fundamental step for optical satellite image applications. Although existing deep learning method can provide more accurate cloud detection result. However, performance of these methods rely 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 due to cloud and high brightness object have a similar spectral feature. 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 cloud detection in high brightness scenes. The label samples are automatically generated from 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 cloud detection in high brightness scenes. The results show that the proposed SSCA-net achieved good performance with average overall accuracy of 97.69% and average Kappa coefficient of 92.71% on Sentinel-2 and Landsat-8 dataset. This paper provides a fresh insight into how advanced deep attention network and cloud index can be integrated to obtain high accuracy of cloud detection on high brightness scenes.