Cross-Image Federated Learning for Hyperspectral Image Classification

The contemporary research paradigm in remote sensing hyperspectral monitoring increasingly relies on multisatellite and multiplatform Earth observation. While the traditional hyperspectral research framework based on single-image processing (SIP) has facilitated the application of idealized scenarios and the development of standardized evaluation benchmarks, it inherently constrains the model's ability to generalize feature representations across varying spatial and temporal domains. As hyperspectral data applications grow in complexity and data requirements, the limitations of SIP in addressing the demands of modern remote sensing tasks become increasingly apparent. To overcome these research limitations, we utilize the decentralized nature and data security features of federated learning to propose a cross-image hyperspectral image (HSI) federated learning approach for classification tasks. We first develop a client-oriented self-guided knowledge-enhanced personalized learning method that enhances the personalization of the local learning process by leveraging relevant features from other clients, thereby improving the learning efficiency of each client. To address the issue of "bias" in global knowledge caused by uneven data distribution across the federated learning process, we introduce a multiscale semantic aligned dynamic aggregation method to ensure fairness in integrating global knowledge. To our knowledge, this article is the first to explore the joint learning of HSI classification using federated learning. Accordingly, we have constructed open-set and closed-set datasets tailored to this task and have demonstrated the effectiveness of our method on these datasets. The code is available at: https://github.com/Gallipaxi/FedHIC.