Abstract
Ultra-high Spatial Resolution Land Cover Classification is essential for fine-grained land cover analysis, yet it remains challenging due to the high cost of pixel-level annotations, significant scale variation, and the limited adaptability of large-scale vision models. Existing methods typically focus on 1-meter spatial resolution imagery and rely heavily on annotated data, whereas practical applications often require processing higher-resolution imagery under weak supervision. To address this, we propose a parameter-efficient semi-supervised segmentation framework for 0.3 m spatial resolution imagery, which leverages the knowledge of SAM2 and introduces a remote sensing-specific FreqWeaver Adapter to enhance fine-grained detail modeling while maintaining a lightweight design at only 5.96% of the total model parameters. By effectively leveraging unlabeled data and maintaining minimal parameter overhead, the proposed method delivers robust segmentation results with superior structural consistency, achieving a 1.78% improvement over existing parameter-efficient tuning strategies and a 3.44% gain compared to state-of-the-art high-resolution remote sensing segmentation approaches.
Abstract (translated)
超高空间分辨率的土地覆盖分类对于精细土地覆盖分析至关重要，但由于像素级标注成本高、尺度变化显著以及大规模视觉模型适应性有限等原因，这一任务仍然具有挑战性。现有方法通常集中在1米分辨率的影像上，并且严重依赖于注释数据，而实际应用往往需要在弱监督条件下处理更高分辨率的影像。为了解决这个问题，我们提出了一种参数高效的半监督分割框架，用于0.3米空间分辨率的影像。该框架利用了SAM2的知识，并引入了一个针对遥感特定需求设计的FreqWeaver适配器，以增强细粒度细节建模的同时保持轻量级设计（仅占总模型参数的5.96%）。通过有效利用未标注数据并维持最小的参数开销，所提出的方法能够提供结构一致性更强且稳健的分割结果，在现有的参数高效调优策略中提升了1.78%，相比最先进的高分辨率遥感分割方法也提高了3.44%。
URL
https://arxiv.org/abs/2506.15565
