关键词: 高光谱图像分类, 三维残差多层融合网络, 自注意力机制, Swin Transformer, 空谱联合特征

Abstract: Convolutional neural networks(CNNs) are widely seen in in hyperspectral image classification due to their remarkably good local context modeling performance.However,under its inherent limitations of network structure,it fails to exploit and represent sequence attributes from spectral characteristics.To address this problem,an integrated novel network,based on Swin Transformer and 3D residual multi-layer fusion network model,is proposed for hyperspectral image classification.In order to excavate the deep features of hyperspectral images as much as possible,spatial spectrum is extracted by improved 3D residual multi-layer fusion network in ReSTrans network,and the context information in consecutive spectra is captured by self-attention mecha-nism Swin Transformer network model.The final result of classification is obtained by multi-layer perception based on spatial spectrum joint feature.In order to verify the effectiveness of the ReSTrans network model,the improved model is experimentally verified on three hyperspectral data sets of IP,UP and KSC,and the classification accuracy reaches 98.65 %,99.64% and 99.78% respectively.Compared with SST method,the classification performance of the network model improves by 3.55%,0.68% and 1.87% respectively.Experimental results show that the model had good generalization ability and could extract deeper and discriminative features.

Key words: Hyperspectral image classification, 3D residual multilayer fusion network, Self-attention mechanism, Swin Transfor-mer, Spatial spectrum joint feature