A novel methodology to address the intricacies of real-world digital surface model super-resolution, named REAL-GDSR, breaking down this ill-posed problem into two steps: a residual local refinement network and an edge-enhancing diffusion technique.
The core message of this paper is to propose two efficient semi-supervised learning architectures, DiverseHead and DiverseModel, that leverage multi-head and multi-model approaches to enhance the precision and diversity of pseudo labels during training, leading to improved semantic segmentation performance on remote sensing imagery datasets.