The rich spectral information stored in every pixel of hyperspectral imaging (HSI) has made it popular in various real-world applications. A nonlinear connection between the correlated HSI data item and the generated spectral data significantly produces complex classification results that are difficult to achieve using conventional methods. Researchers have recognized that only spectral information is insufficient to classify HSI data, so spatial information needs to be incorporated to improve the classification outcomes. This paper employs BERT and ALBERT models, recently proposed in natural language processing (NLP), as feature extraction models to acquire spectral information. These models use spectral signatures to learn the relationship between the pixels and their neighbors, and they seek to understand the context by learning the relationship between tokens. Despite this, BERT and ALBERT might not effectively use spatial information. This paper incorporates the spatial information with BERT and ALBERT and proposed Spatial-BERT and Spatial-ALBERT. Here, we efficiently integrate each target pixel’s spatial and spectral information in the HSI data for these models to improve the classification performance. The experimental results obtained on a publicly available dataset, the University of Pavia, demonstrate that the Spatial-ALBERT model achieves satisfactory performance with few parameters and relatively better efficiency than the Spatial-BERT model. The results show that Spatial-ALBERT outperformed the existing CNN and RNN-based methods and performed better in twelve out of twenty tests than Spatial-BERT.