Remote sensing semantic segmentation (RSS) is an essential technology in earth observation missions. Due to concerns over geographic information security, data privacy, storage bottleneck and industry competition, high-quality annotated remote sensing images are often isolated and distributed across institutions. The issue of remote sensing data islands poses challenges for fully utilizing isolated datasets to train a global model. Federated learning (FL), a privacy-preserving distributed collaborative learning technology, offers a potential solution to leverage isolated remote sensing data. Typically, remote sensing images from different institutions exhibit significant geographic heterogeneity, characterized by coupled class-distribution heterogeneity and object-appearance heterogeneity. However, existing FL methods lack consideration of them, leading to a decline in the performance of the global model when FL is directly applied to RSS. We propose a novel Geographic heterogeneity-aware Federated learning (GeoFed) framework to bridge data islands in RSS. Our framework consists of three modules, including the Global Insight Enhancement (GIE) module, the Essential Feature Mining (EFM) module and the Local-Global Balance (LoGo) module. Through the GIE module, class distribution heterogeneity is alleviated by introducing a prior global class distribution vector. We design an EFM module to alleviate object appearance heterogeneity by constructing essential features. Furthermore, the LoGo module enables the model to possess both global generalization capability and local adaptation. Extensive experiments on three public datasets (i.e., FedFBP, FedCASID, FedInria) demonstrate that our GeoFed framework consistently outperforms the current state-of-the-art methods.

翻译：遥感语义分割是地球观测任务中的一项关键技术。由于地理信息安全、数据隐私、存储瓶颈及行业竞争等因素，高质量的标注遥感影像往往被隔离并分散存储于不同机构。遥感数据孤岛问题对充分利用孤立数据集训练全局模型构成了挑战。联邦学习作为一种保护隐私的分布式协同学习技术，为利用孤立的遥感数据提供了潜在解决方案。通常，来自不同机构的遥感影像表现出显著的地理异构性，其特征表现为类别分布异构性与目标外观异构性的耦合。然而，现有联邦学习方法缺乏对此类异构性的考量，导致将联邦学习直接应用于遥感语义分割时全局模型性能下降。本文提出一种新颖的地理异构感知联邦学习框架，以连接遥感语义分割中的数据孤岛。该框架包含三个模块：全局洞察增强模块、本质特征挖掘模块以及局部-全局平衡模块。通过全局洞察增强模块，我们通过引入先验全局类别分布向量缓解类别分布异构性问题。我们设计了本质特征挖掘模块，通过构建本质特征来缓解目标外观异构性。此外，局部-全局平衡模块使模型同时具备全局泛化能力与局部适应能力。在三个公开数据集上的大量实验表明，本框架在性能上持续优于当前最先进的方法。