Transformers Fusion across Disjoint Samples for Hyperspectral Image Classification

arXiv:2405.01095v1 Announce Type: new
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 …

abstract arxiv attention attention mechanisms classification cs.cv dependencies eess.iv fusion hierarchical image images modeling novel paper processing relationships samples self-attention spatial swin swin transformer through transformer transformers type