Global and Local Dual Fusion Network for Large-ratio Cloud Occlusion Missing Information Reconstruction of a High-resolution Remote Sensing Image

Large-ratio cloud occlusion significantly hampers the utilization of high-resolution remote sensing imagery. The existing reconstruction methods (1) overlook the problem of reconstructed and composite images sharing high-and low-level semantic and visual attributes in non-reconstructed regions, exacerbating the pronounced boundary effects; (2) neglect appearance discrepancies between reconstructed and non-reconstructed regions, leading to spectral degradation, and texture loss; and (3) overlook the problem of reconstructing large-ratio missing information. To address these issues, a global and local dual fusion network is proposed in this study for large-ratio cloud occlusion removal in high-resolution remote sensing images. The global foreground–background aware attention module tackles shared high-level semantic features, whereas the local visual feature enhancement module addresses appearance differences. The global and local dual fusion network combines the Sobel and reconstruction loss functions for effective reconstruction by employing a two-stage fusion strategy. Compared to the classical recurrent feature reasoning network, spatiotemporal generator network, spatial-temporal-spectral convolutional neural network, and bishift network, the proposed model demonstrates superior quantitative and visual reconstruction outcomes for the 40%, 50%, and 70% missing ratios of Gaofen-1 (2 m).