GeCoM: Joint Generative and Interactive Contrastive Self-Supervised Learning for Few-Shot Classification of Multi-Source Remote Sensing Imagery

Accurate image classification under extreme label scarcity remains a critical challenge in remote sensing, where hyperspectral imagery offers rich spectral signatures but suffers from coarse spatial resolution limitations. Although integrating complementary panchromatic and multispectral data can alleviate this spatial bottleneck, existing methods struggle to fully exploit their deep spatial–spectral features without dense annotations. We propose GeCoM, a joint generative and interactive contrastive self-supervised learning framework for few-shot multi-source image classification. GeCoM employs modality-specific Transformer encoders with a multi-scale masking strategy to reconstruct fine spatial–spectral patterns. Simultaneously, an interactive contrastive optimization module is introduced to align heterogeneous multi-source features within a unified latent space. Experiments on the Houston 2018 benchmark and two regional scenes from Gaofen-14 satellite demonstrate that GeCoM achieves superior Overall Accuracies of 74.66%, 92.36%, and 94.02% with only 15 labeled samples per class, outperforming state-of-the-art baselines. This study provides a robust, label-efficient spatial–spectral representation solution for multi-sensor earth observation.