Large-scale high-resolution land cover classification is a prerequisite for
constructing Earth system models and addressing ecological and resource issues.
Advancements in satellite sensor technology have led to an improvement in
spatial resolution and wider coverage areas. Nevertheless, the lack of
high-resolution labeled data is still a challenge, hindering the largescale
application of land cover classification methods. In this paper, we propose a
Transformerbased weakly supervised method for cross-resolution land cover
classification using outdated data. First, to capture long-range dependencies
without missing the fine-grained details of objects, we propose a U-Net-like
Transformer based on a reverse difference mechanism (RDM) using dynamic sparse
attention. Second, we propose an anti-noise loss calculation (ANLC) module
based on optimal transport (OT). Anti-noise loss calculation identifies
confident areas (CA) and vague areas (VA) based on the OT matrix, which
relieves the impact of noises in outdated land cover products. By introducing a
weakly supervised loss with weights and employing unsupervised loss, the
RDM-based U-Net-like Transformer was trained. Remote sensing images with 1 m
resolution and the corresponding ground-truths of six states in the United
States were employed to validate the performance of the proposed method. The
experiments utilized outdated land cover products with 30 m resolution from
2013 as training labels, and produced land cover maps with 1 m resolution from
2017. The results show the superiority of the proposed method compared to
state-of-the-art methods. The code is available at
https://github.com/yu-ni1989/ANLC-Former