Convolutional neural networks (CNNs) are widely used for hyperspectral image (HSI) classification. However, the high spatial and spectral dimensionality of HSIs often leads to significant computational costs and challenges during network training. Moreover, CNNs are limited in capturing high-level semantic features. In contrast, transformer models are better suited to modeling high-level semantic information and capturing long-range dependencies, making them a promising approach for HSI classification. In this paper, we propose a novel HSI classification framework, LSKTT, which integrates a la...
