轻量级模型实现遥感图像实时分割，兼顾精度与效率
GeoSAM-Lite: A Lightweight Foundation Model for Onboard Remote Sensing Segmentation
- 无提示设计+轻量化结构，适配边缘设备部署
- 参数减少92.8%，在云场景下保持高精度
- 适合遥感卫星、无人机等资源受限场景
将大规模基础模型如分割一切模型（SAM）部署于资源受限的地球观测平台时，面临计算成本过高及自然图像与遥感图像之间的领域差异问题。为此，我们提出地理空间分割一切模型-轻量版（GeoSAM-Lite），一种面向高效机载遥感分割的轻量化、无提示分割框架。其核心创新包括：(1) 地理空间域初始化（Geo-Init），通过专用教师模型提取地理空间先验，弥合领域差距；(2) 特征融合层（FFL），重校准空间特征并恢复高频边界信息，克服轻量骨干网络的能力瓶颈。在代表性数据集上的实验表明，尤其在云场景下评估极端尺度变化和复杂边界时，GeoSAM-Lite在参数量减少92.8%的前提下，仍达到与重型模型RSAM-Seg相当的准确率。通过建立效率与保真度间的更优帕累托前沿，为边缘设备上的实时分割提供可行方案。
原文摘要 · Abstract (English)
The deployment of large-scale foundation models like Segment Anything Model (SAM) on resource-constrained Earth observation platforms is hindered by prohibitive computational costs and the domain shift between natural and remote sensing imagery. To address these challenges, we propose \textit{Geo}spatial \textit{S}egment \textit{A}nything \textit{M}odel-Lite (GeoSAM-Lite), a lightweight, prompt-free segmentation framework designed for efficient onboard remote sensing segmentation. GeoSAM-Lite incorporates two core innovations: (1) Geospatial-Domain Initialization (Geo-Init), a domain-aware pre-training strategy that distills geospatial priors from a specialized teacher to bridge the domain gap; and (2) Feature Fusion Layers (FFL), which recalibrate spatial features and restore high-frequency boundary cues to overcome the capacity bottlenecks of lightweight backbones. Experiments across representative datasets, with a primary focus on cloud scenarios to evaluate performance under extreme scale variations and complex boundaries, demonstrate that GeoSAM-Lite achieves competitive accuracy while reducing parameters by 92.8\% compared to the heavyweight RSAM-Seg. By establishing a superior Pareto frontier between efficiency and fidelity, GeoSAM-Lite offers a practical solution for real-time segmentation on edge devices.
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