Self-Attention and Mutual-Attention for Few-Shot Hyperspectral Image Classification

Kai Huang, Xinyang Deng, Jie Geng, Wen Jiang

Abstract: Few-shot classification of hyperspectral image (HSI) has been increasingly abstracted attention due to its superiority of adopting to new HSI classification with only a few labeled data available. However, insufficient feature expression still bothers the improvement of performance. To address this issue, a deep self-attention and mutual-attention few-shot learning (SMA-FSL) method is proposed for HSI few-shot classification. Specifically, a deep 3D convolutional feature embedding network is utilized to extract the spectral-spatial feature at first. Then, self-attention and mutual-attention are used to ally the feature of different samples with same class and expand the class prototypes for more stable feature expression. Finally, the predicted results are obtained by calculating the distance between query set and aligned class prototypes. The experimental results on two well-know HSI datasets demonstrate that the proposed method achieves better performance compared with other related methods.