Transformers Fusion across Disjoint Samples for Hyperspectral Image Classification

TL;DR

This paper proposes a novel attentional fusion of 3D Swin Transformer and Spatial-spectral Transformer to improve hyperspectral image classification, emphasizing disjoint sample training for robustness and superior performance.

Contribution

It introduces a new fusion method combining two transformer architectures specifically for hyperspectral image classification, emphasizing disjoint sample training for robustness.

Findings

Fusion approach outperforms traditional methods

Disjoint training enhances robustness

Achieves higher classification accuracy

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.