A spatial-spectral-frequency interactive network for multimodal remote sensing classification

Deep learning-based methods have achieved significant success in remote sensing Earth observation data analysis. Numerous feature fusion techniques address multimodal remote sensing image classification by integrating global and local features. However, these techniques often struggle to extract structural and detail features from heterogeneous and redundant multimodal images, particularly in label-scarce scenarios. With the goal of introducing frequency domain learning to model key and sparse detail features, this paper introduces the spatial–spectral-frequency interaction network (S 2 Fin), which integrates pairwise fusion modules across the spatial, spectral, and frequency domains. Specifically, we propose a high-frequency sparse enhancement transformer to refine spectral signatures by adaptively enhancing discriminative high-frequency components. For spatial-frequency interaction, we present a depth-wise strategy: the adaptive frequency channel module fuses low-frequency structural information with enhanced details in shallow layers, while the high-frequency resonance mask amplifies modality-consistent regions in deep layers using phase similarity. In addition, a spatial–spectral attention fusion module bridges the gap between spectral and spatial branches at intermediate depths. Extensive experiments on four benchmark datasets demonstrate that S 2 Fin exhibits good robustness and generalization, and its performance significantly outperforms state-of-the-art methods in few-sample settings. The code is available at https://github.com/HaoLiu-XDU/SSFin .