Improving Cloud/Snow Detection in Remote Sensing Image with Spatiotemporal Information Fusion

Abstract

The clouds and snow in optical remote sensing images always interfere with the interpretation of remote sensing images, which even makes an entire image unavailable. In general, the proportion of cloud/snow cover in remote sensing images needs to be clarified to improve the utilization of remote sensing images. The metadata of remote sensing image products contains prior knowledge of spatiotemporal information, such as imaging time, latitude and longitude, and altitude. This paper proposes a remote sensing image cloud/snow detection method that fuses spatial and temporal information. The proposed method can combine spatiotemporal information for feature extraction and stitching, thus improving the accuracy of remote sensing image cloud/snow detection. In this study, the proposed method is trained and tested with a large-scale cloud/snow image dataset. The experimental results show that both the temporal or spatial information alone and the fused temporal and spatial information can improve the cloud/snow detection accuracy in remote sensing images. The easy-to-obtain imaging time information can also significantly improve the detection accuracy for cloud/snow. The proposed method can be used to improve the cloud/snow detection effect of any remote sensing image product containing prior knowledge of spatiotemporal information and has a good application prospect.