Recent advances in deep learning for hyperspectral image classification have shown exceptional performance in resource management and environmental planning through land use classification. Despite these successes, challenges continue to persist in land use classification due to the complex topology of natural and man-made structures. The uneven distribution of land cover introduces spectral-spatial variability, causing inter- and intra-class similarity. To address this issue, this study adopts a hybrid approach that combines convolutional neural networks and a transformer model. The technique comprises three key components: a spectral-spatial convolutional module (SSCM), a spatial attention module (SAM), and a transformer module (TM). Each component facilitates the others in the process of classification. SSCM is used to extract shallow features with the help of dilated convolutional layers, while the SAM enhances spatial features for further processing. In addition, a TM with a local neighborhood attention mechanism is employed to extract local semantic information. Several experiments conducted on the Indian Pines and Pavia University hyperspectral datasets validate the performance of the proposed technique, demonstrating higher classification accuracy compared to recent methods in the literature. The technique achieves average accuracies of 97.24% and 99.33% on the Indian Pines and Pavia University datasets, respectively, thus demonstrating its effectiveness for land resource management and environmental planning.