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Classifying hyperspectral images in remote sensing applications is challenging due to limited training samples and high dimensionality of data. Deep-learning-based methods have recently demonstrated promising results in the classification of HSI. This article presents a proposed methodology for extracting local features and high-level semantic features from HSI input data using a light-weighted spectral&#x2013;spatial transformer. This approach will allow us to comprehensively examine the spatial and spectral characteristics while reducing the computing expenses. The proposed model integrates lightweight multihead self-attention and residual feedforward modules in order to effectively capture long-range dependencies and address the computational challenges associated with this model. In order to assess the efficiency <italic>of the proposed model</italic>, we conducted experiments on four publicly available datasets. The obtained experimental results were then compared with those of the existing state-of-the-art models. The proposed model obtains the best classification results in terms of classification accuracy and computational complexity under limited training samples. The overall accuracy of the proposed model achieved <inline-formula><tex-math notation="LaTeX">$99.91\%,\;98.06\%,\;99.43\% $</tex-math></inline-formula> and <inline-formula><tex-math notation="LaTeX">$99.01\% $</tex-math></inline-formula> on four datasets.