Research on the Cloud Removal of Remote Sensing Images Based on the GAN Network
This article mainly studies a crucial issue in the application of remote sensing images,especially the situa-tion that cloud occlusion can lead to the degradation of data accuracy.In order to address this problem,this article uses group convolution,the multi-head self-attention mechanism and the generative adversarial network to con-struct an end-to-end cloud removal network of remote sensing images.Experiments on the RICE dataset demon-strate that the proposed approach has a significant effect in solving the problem of cloud occlusion,and successfully generates clear and cloud-free remote sensing images,which provides an effective solution for the field of hydraulic engineering and remote sensing image processing overall,and especially has its obvious advantages in the utilization,computation and parameter optimization of spatial information.
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