LFAH-Net: Laplacian frequency aware hierarchical network for hyperspectral image classification

In recent years, the combination of convolutional neural networks (CNNs) with transformers for spectral-spatial feature extraction and robust semantic modeling has greatly improved the performance in hyperspectral image (HSI) classification tasks. However, these methods often overlook frequency information; CNNs struggle to capture global dependencies due to limited receptive fields, and transformers tend to lose fine-grained local structures and high-frequency variations. To address these challenges, this paper proposes a Laplacian frequency aware hierarchical network (LFAH-Net). We first design the method employing a diversity frequency-aware transformer (DFAT) module alongside a multi-level frequency fusion block (MFFB) stack to explicitly separate and integrate high-frequency signals such as edges and textures, as well as low-frequency signals like spectral contours, thereby achieving cross-level frequency feature complementarity. Besides, we propose a spectral-spatial adaptive recalibration fusion (SSARF) module, specifically designed to correct misalignments and suppress noise in hyperspectral features. Finally, the multi-scale dilation convolution (MSDC) module utilizes dilated convolutions to capture both local and global contextual information, while the adaptive feature fusion (AFF) module adaptively recalibrates and fuses these features with the spectral representations from DFAT. Experimental results on four popular hyperspectral datasets demonstrate that our framework significantly outperforms several state-of-the-art methods.