Transformers Fusion across Disjoint Samples for Hyperspectral Image Classification

Abstract

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.

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Cited by in corpus (5)

• Spatial and Spatial-Spectral Morphological Mamba for Hyperspectral Image Classification
• WaveMamba: Spatial-Spectral Wavelet Mamba for Hyperspectral Image Classification
• DiffFormer: a Differential Spatial-Spectral Transformer for Hyperspectral Image Classification
• Multi-head Spatial-Spectral Mamba for Hyperspectral Image Classification
• Transformer-Driven Active Transfer Learning for Cross-Hyperspectral Image Classification