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this paper, we propose a Transformerbased weakly supervised method for cross-resolution land cover classification using outdated data.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identifies confident areas (CA) and vague areas (VA) based on the OT matrix, which relieves the impact of noises in outdated land cover products.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 The experiments utilized outdated land cover products with 30 m resolution from 2013 as training labels, and produced land cover maps with 1 m resolution from 2017.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.

翻译：大规模高分辨率土地覆盖分类是构建地球系统模型、解决生态与资源问题的前提。卫星传感器技术的进步提升了空间分辨率并扩大了覆盖范围，但高分辨率标注数据的匮乏仍是挑战，阻碍了土地覆盖分类方法的大规模应用。本文提出一种基于Transformer的弱监督方法，利用过时数据进行跨分辨率土地覆盖分类。首先，为在不遗漏目标细粒度细节的前提下捕捉长距离依赖，我们提出一种基于反向差分机制（RDM）的U型类Transformer结构，采用动态稀疏注意力。其次，我们提出基于最优传输（OT）的抗噪损失计算（ANLC）模块。抗噪损失计算依据OT矩阵识别可信区域（CA）与模糊区域（VA），从而减轻过时土地覆盖产品中噪声的影响。通过引入带权重的弱监督损失并结合无监督损失，对基于RDM的U型类Transformer进行训练。采用美国六个州的1米分辨率遥感影像及其对应真实标签验证所提方法性能。实验以2013年的30米分辨率过时土地覆盖产品作为训练标签，生成2017年的1米分辨率土地覆盖图。结果表明，所提方法相较于现有最优方法具有优越性。代码已开源至https://github.com/yu-ni1989/ANLC-Former。