Cloud Detection for Remote Sensing Images Based on Difference Features and Semantic Segmentation Network

High-precision cloud detection for remote sensing image is of great significance for applications in agriculture, environment, meteorology and other fields. Cloud detection in bright surface environments and thin clouds identification have always been a challenge in cloud detection research. Aiming at this problem, a cloud detection method for remote sensing images based on difference features and semantic segmentation network is proposed in this paper. Cloudy and cloudless images of the same area are used to obtain difference features, and cloudless images are used as the surface reference. The multi-band cloud detection network based on U-network (U-net) fully learns the difference feature between clouds and the surface, and combines the feature information of the shallow and deep layers to accurately identify the cloud and the surface. Experiments were conducted on the Landsat 8 validation dataset. The results show that the proposed method achieves good performance. It improves the detection accuracy and reduces the misclassification over the bright surface underlying compared with the method based on top of atmosphere reflectance (TOA).