Two-timer-KAN: Dual-Exclusive Fourier KANs with Gaussian Fusion for Few-Shot Multimodal Remote Sensing Imagery Classification.

Abstract

Multimodal remote sensing imagery classification (MRSIC) aims to synergistically leverage complementary information from heterogeneous data sources, enabling precise land-cover classification. Existing MRSIC approaches predominantly rely on abundant annotated samples, facing critical performance degradation under data-scarce scenarios that are particularly exacerbated by the inherent complexity of heterogeneous multi-modal data. Furthermore, effectively extracting spatial-spectral information of multimodal data and fusing the cross-modal heterogeneous features persists as a significant challenge. To address these obstacles, we propose a pioneering few-shot MRSIC network, Two-timer-KAN, which integrates modality-specific feature extraction for spectral- and spatial-dominant data. Specifically, leveraging the nonlinear power of Kolmogorov-Arnold Networks (KANs), we develop the Dual-Exclusive Fourier KAN (DEF-KAN) encoder, which captures modality-specific global features in the frequency domain, bridging spectral and spatial gaps across various datasets. Following this, a Multivariate-Gaussian-based Cross-KAN (MG-Cross-KAN) is dedicated to enhancing the robustness of cross-modality fusion by capturing modality-shared features in a distribution-based manner. Finally, to further tackle classification ambiguity under limited annotated samples, we present a visual-textual bidirectional alignment strategy, which leverages textual descriptions as supplementary semantical knowledge to clarify class feature centers. Extensive experiments demonstrate that the proposed Two-timer-KAN achieves superior performance, outperforming the state-of-the-art methods in both accuracy and robustness.