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Convolutional neural networks are a popular method in hyperspectral image classification. However, the accuracy of the models is closely related to the number and spatial size of training samples. Which relieve the performance decline by the number and spatial size of training samples, we designed a 3D multi-head self-attention spectral-spatial feature fusion network (3DMHSA-SSFFN) that contains step-by-step feature extracted blocks (SBSFE) and 3D multi-head-self-attention-module (3DMHSA). The proposed step-by-step feature extracted blocks relieved the declining-accuracy phenomenon for the limited number of training samples. Multi-scale convolution kernels extract more spatial-spectral features in the step-by-step feature-extracted blocks. In hyperspectral image classification, the 3DMHSA module enhances the stability of classification by correlating disparate features. Experimental results show that 3DMHSA-SSFFN possesses a better classification performance than other advanced models through the limited number of balance and imbalance training data in three data.

Hyperspectral Image Classification Based on 3D Multi-Head Self-Attention Spectral–Spatial Feature Fusion Network