Rethinking Efficient Mixture-of-Experts for Remote Sensing Modality-Missing Classification

The article discusses the development of a frequency-aware vision-language multimodality generalization network (FVMGN) aimed at improving remote sensing image classification. This new approach addresses the challenges posed by data heterogeneity and the limitations of existing vision-language models (VLMs), which often rely on universal texts that do not account for specific linguistic knowledge related to different remote sensing modalities. The proposed method includes a diffusion-based training-test-time augmentation strategy and a multimodal wavelet disentanglement module.