In high-resolution remote sensing images,missing areas feature intricate surface features and pronounced spatial heterogeneity,causing the image inpainting results to suffer texture blurring and structural distortion,particularly for boundaries and areas with complex textures.This study proposed a two-stage remote sensing image inpainting network combined with spatial semantic attention(SSA).The network comprised two networks in series:one for coarse image inpainting and one for fine-scale image inpainting(also referred to as the coarse and fine-scale networks,respectively).This network was designed to guide the fine-scale network to restore the missing areas using the priori information provided by the coarse network.In the coarse network,a multi-level loss structure was constructed to enhance the stability of network training.In the fine-scale network,a novel SSA mechanism was proposed,with SSA being embedded differentially in the encoder and decoder based on the distribution of network features.This ensured the continuity of local features and the correlation of global semantic information.The experimental results show that the network proposed in this study can further improve the image inpainting effects compared to other existing algorithms.
关键词
二段式网络/遥感图像修复/空间语义注意力/局部特征连续性/全局语义信息相关性
Key words
two-stage network/remote sensing image inpainting/spatial semantic attention/continuity of local fea-tures/correlation of global semantic information
维普
万方数据