SAFE框架提升遥感协同感知，解决数据分散与通信难题。
SAFE: Self-Adjustment Federated Learning Framework for Remote Sensing Collaborative Perception
- 通过自调整机制应对不同卫星间数据分布差异。
- 在真实遥感数据集上实现分类与目标分割性能显著提升。
- 适合需要隐私保护的分布式遥感系统应用。
遥感卫星数量激增催生了分布式天基观测系统。现有分布式遥感模型多依赖集中式训练，导致数据泄露、通信开销大及因平台间数据分布差异造成的精度下降。为此，本文提出自调整联邦学习框架（SAFE），创新性地利用联邦学习提升遥感场景下的协同感知能力。SAFE引入四项关键策略：(1) 类别修正优化，自主处理未知本地与全局分布下的类别不平衡问题；(2) 特征对齐更新，通过局部控制的EMA更新缓解非独立同分布（Non-IID）数据问题；(3) 双因素调制电位器，动态平衡训练过程中的优化效果；(4) 自适应上下文增强，通过动态优化前景区域提升模型性能，在保证计算效率的同时实现精度提升。在真实世界图像分类与目标分割数据集上的实验验证了SAFE框架在复杂遥感场景下的有效性与可靠性。
原文摘要 · Abstract (English)
The rapid increase in remote sensing satellites has led to the emergence of distributed space-based observation systems. However, existing distributed remote sensing models often rely on centralized training, resulting in data leakage, communication overhead, and reduced accuracy due to data distribution discrepancies across platforms. To address these challenges, we propose the \textit{Self-Adjustment FEderated Learning} (SAFE) framework, which innovatively leverages federated learning to enhance collaborative sensing in remote sensing scenarios. SAFE introduces four key strategies: (1) \textit{Class Rectification Optimization}, which autonomously addresses class imbalance under unknown local and global distributions. (2) \textit{Feature Alignment Update}, which mitigates Non-IID data issues via locally controlled EMA updates. (3) \textit{Dual-Factor Modulation Rheostat}, which dynamically balances optimization effects during training. (4) \textit{Adaptive Context Enhancement}, which is designed to improve model performance by dynamically refining foreground regions, ensuring computational efficiency with accuracy improvement across distributed satellites. Experiments on real-world image classification and object segmentation datasets validate the effectiveness and reliability of the SAFE framework in complex remote sensing scenarios.
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