Very high resolution (VHR) satellite and aerial images often suffer from scene occlusion caused by redundant objects. The task of removing these redundant objects can be solved by missing data reconstruction technology. However, when dealing with VHR images with large-scale missing regions, existing spatial-based methods often destroy the structural information of ground objects. To alleviate this problem, this paper proposes a novel missing data reconstruction method based on deep learning. The reconstruction process is divided into two parts: structure prediction and texture generation. First, a progressive edge generation network (PEGN) is designed to predict the edges of objects in missing regions in a progressive manner. Then, the edge map predicted by PEGN is input to a texture generation network (TGN) as structural information to produce the reconstruction results. This is a spatial-based method that can produce realistic and reasonable results without any need for auxiliary spectral or temporal data. Experiments demonstrate that our model can better restore the structure of ground objects in VHR images than other spatial-based methods and outperform them in SSIM and PSNR indices. In addition, our model also has a strong generalization capability by introducing Poisson blending and histogram matching.

高分辨率（VHR）卫星和航拍图像通常会因多余物体而导致场景遮挡。可以通过缺少数据重建技术来解决删除这些冗余对象的任务。然而，当处理具有大面积缺失区域的VHR图像时，现有的基于空间的方法通常会破坏地面物体的结构信息。为了缓解这个问题，本文提出了一种基于深度学习的新颖的缺失数据重构方法。重建过程分为两部分：结构预测和纹理生成。首先，设计渐进边缘生成网络（PEGN）以渐进方式预测缺失区域中对象的边缘。然后，将PEGN预测的边缘图作为结构信息输入到纹理生成网络（TGN），以生成重建结果。这是一种基于空间的方法，无需任何辅助频谱或时间数据即可产生现实且合理的结果。实验表明，与其他基于空间的方法相比，我们的模型可以更好地恢复VHR图像中地面对象的结构，并且在SSIM和PSNR指数方面优于它们。此外，通过引入泊松混合和直方图匹配，我们的模型还具有很强的泛化能力。实验表明，与其他基于空间的方法相比，我们的模型可以更好地恢复VHR图像中地面对象的结构，并且在SSIM和PSNR指数方面优于它们。此外，通过引入泊松混合和直方图匹配，我们的模型还具有很强的泛化能力。实验表明，与其他基于空间的方法相比，我们的模型可以更好地恢复VHR图像中地面对象的结构，并且在SSIM和PSNR指数方面优于它们。此外，通过引入泊松混合和直方图匹配，我们的模型还具有很强的泛化能力。