FDM-Net: A Multi-Level Feature Aggregation Network Based on Frequency-Decomposition for Hyperspectral Image Classification

Abstract

Recently, integrating convolutional neural networks (CNNs) with Mamba has shown notable advantages in hyperspectral image classification. However, existing Mamba–CNN hybrid frameworks typically adopt a parallel-branch architecture where identical spectral–spatial information is fed into both branches, failing to rectify the inherent frequency-specific bias: Mamba tends to prioritize low-frequency information, while CNNs excel at capturing high-frequency details. Existing convolutional architectures often fail to effectively exploit the multi-level interactions among spectral–spatial features. To handle these limitations, a novel multi-level Aggregation Network based on Frequency Decomposition (FDM-Net) is proposed. Specifically, a Frequency Decomposition Fusion Enhancement (FDE) strategy first splits the features into low- and high-frequency components and then applies spatial-frequency gating and interactive enhancement to refine the decomposed features. A Mamba-based module is then integrated into the low-frequency branch to model long-range spatial dependencies. Meanwhile, a Multi-Level Feature Aggregation Module (MLFA) leverages multi-level depthwise convolutions and gated aggregation to capture complex multi-level interactions in high-frequency features. Finally, an adaptive frequency fusion (AFF) module dynamically reintegrates these features, yielding a discriminative spectral–spatial representation that integrates global semantics and local textures. Extensive experiments on five benchmark HSI datasets demonstrate that FDM-Net consistently surpasses state-of-the-art methods, achieving the highest OA and Kappa across all five datasets and the best AA on QHUP (92.35%) and QHUT (93.86%). Notably, FDM-Net outperforms the second-best method by margins of 0.28–1.01% in OA and 0.33–1.14% in Kappa.