Adaptive Mixture-of-Experts Distillation for Cross-Satellite Generalizable Incremental Remote Sensing Scene Classification

Incremental learning aims to continuously acquire new knowledge from data streams while maintaining previously learned knowledge. Existing incremental learning methods typically assume that the training (source domain) and testing (target domain) data are identically distributed. However, differences in sensor parameters and imaging conditions inevitably lead to distribution gaps between data collected from different satellites (domains). The ensuing domain shift problem substantially impairs the generalization of continuously learned knowledge from source domains to unseen ones. To tackle this problem, we propose adaptive mixture-of-experts distillation (AMoED) for cross-satellite generalizable incremental remote sensing scene classification (CSGIRSSC). Specifically, AMoED adopts a high-level semantic learning pipeline, in which new knowledge is acquired through the coordinated guidance of multiple domain-specific experts, rather than directly from raw data. This pipeline prevents the model from being exposed to large volumes of newly emerging data, thereby alleviating the erasure of previous knowledge when adapting to new data distributions. Besides, the adaptive mixture of domain-specific experts facilitates the formation of universal class concepts, which exhibit strong generalizability across different domains. During the learning process, an equi-partite subset is constructed for knowledge acquisition and consolidation, accompanied by a shallow style-mixing operation to mitigate the interference of domain discrepancies. Extensive experiments are conducted on four remote sensing scene classification datasets, and the proposed method consistently achieves state-of-the-art performance across various scenarios and settings.