Attention Mechanism With Spatial Spectrum Dense Connection and Context Dynamic Convolution for Cloud Detection

Rapid advances in remote sensing technology have allowed its extensive use in defense, land use planning, urban traffic monitoring, and natural disaster warning. Remote sensing technology has penetrated every aspect of modern life. However, some problems need to be solved in the use of remote sensing data, such as the presence of clouds in images. Efficient air–ground data transmission can be realized by performing cloud rejection on remote sensing images before satellite data transmission. Therefore, in this study, remote sensing images were analyzed, and an effective cloud detection algorithm was designed. A dense-connected-strategy-based spectral–spatial feature extraction module that can realize the independent extraction of spectral and spatial information was designed. To enhance the effective information and suppress the useless information, spatial and channel attention modules based on the self-attention mechanism were designed and added after the spectral information extraction and spatial information extraction modules, respectively. Finally, the contextual dynamic convolution module was designed to adjust the convolution kernel parameters adaptively and enhance the characterization ability of the network.