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.

本文介绍了一种新颖的方法：将 3D Swin Transformer 和 Spatial-spectral Transformer 的注意力机制进行融合，从而显著提高高光谱图像的分类性能。该方法不仅优化了空间和光谱信息的建模，还实现了更精确和准确的分类结果，通过在基准高光谱图像数据集上进行实验和评估，表明了融合方法的有效性，展示了其相对于传统方法和独立变换器的优越性。同时，采用独立的训练、验证和测试样本增强了所提出方法的鲁棒性和可靠性，并突出了其推动高光谱图像分类领域的潜力。