A novel weakly supervised semantic segmentation framework to improve the resolution of land cover product

Open-source land cover products (LCPs) are essential for many areas of scientific research. However, they have deficiencies such as low accuracy, low resolution, and poor timeliness when applied to a specific area. Therefore, we developed WESUP-LCP, a novel two-stage weakly supervised semantic segmentation framework to improve the resolution of LCPs without any manual labeling cost. In the first stage, we designed a sample transferring module to transfer accurate and representative training samples from low-resolution LCPs. In the second, partially labeled superpixels generated from the transferred labels were used to train the weakly supervised learning network and pseudo-labels were generated through dynamic label propagation during the training process. We designed two experiments to verify the performance of the proposed method, including the experiment on improving the resolution of LCP in specific areas (improving 30 m LCP to 10 m resolution) and the experiment on the public dataset of the multi-temporal semantic change detection task of 2021 IEEE GRSS Data Fusion Contest (improving 30 m LCP to 1 m resolution). The results showed that our method has remarkable advantages over other methods, demonstrating the applicability of WESUP-LCP to improve LCP resolution.