Accurate and lightweight cloud detection method based on cloud and snow coexistence region of high-resolution remote sensing images

Cloud detection is a critical stage in remote sensing image preprocessing. However, when there is snow on the underlying surface of scenes, the general cloud detection methods wouldbe easily affected. As a result, the cloud detection accuracy of these methods would reduce.Furthermore, most available cloud detection datasets are of medium-resolution and do not focus on the cloud and snow coexistence study areas. As a result, a cloud detection dataset has been created and released based on high-resolution cloud-snow coexistence remote sensing images.Meanwhile, this study suggests a convolution neural network termed RDC-Net for cloud detection in high-resolution cloud and snow coexistence images. The RDC-Net contains the reconstructible multiscale feature fusion module for multiscale cloud feature extraction, the dual adaptive feature fusion module for effective cloud feature representation reconstruction, and the controllably deep gradient guidance flows module for unbiased network gradient descent guidance. Benefiting from the above technical components, the network can enhance the robustness of cloud detection in complicated regions and facilitate lightweight deployment of the network. The experimental results show that the RDC-Net has an excellent anti-interference capacity for highlighted ground objects and has outstanding detection performance for thin clouds and clouds over snow. Furthermore, the RDC-Net has fewer parameters and floating-point operations, making it acceptable for industrial production and application. © 2023 SinoMaps Press. All rights reserved.