  3D Swin Transformer (3D-ST) known for its hierarchical attention and
window-based processing, excels in capturing intricate spatial relationships
within images. Spatial-spectral Transformer (SST), meanwhile, specializes in
modeling long-range dependencies through self-attention mechanisms. Therefore,
this paper introduces a novel method: an attentional fusion of these two
transformers to significantly enhance the classification performance of
Hyperspectral Images (HSIs). What sets this approach apart is its emphasis on
the integration of attentional mechanisms from both architectures. This
integration not only refines the modeling of spatial and spectral information
but also contributes to achieving more precise and accurate classification
results. The experimentation and evaluation of benchmark HSI datasets
underscore the importance of employing disjoint training, validation, and test
samples. The results demonstrate the effectiveness of the fusion approach,
showcasing its superiority over traditional methods and individual
transformers. Incorporating disjoint samples enhances the robustness and
reliability of the proposed methodology, emphasizing its potential for
advancing hyperspectral image classification.
