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 des...
