Grid-transformer for few-shot hyperspectral image classification

The application of few-shot learning to hyperspectral image (HSI) classification tasks has gradually become a research hotspot due to the difficulties in acquiring and labeling HSI data. Existing methods tend to cascade a large number of convolutional neural networks. However, such operations can only focus on local information and cannot accurately capture the strong correlation between spectra. To address this problem, we propose Grid-transformer, an efficient spatial-spectral feature extraction model. Specifically, we first introduce a more powerful transformer to compute non-local self-similarity along the spectral dimension, which is beneficial to mine more discriminative spectral features. Then, they are embedded into a grid-like network architecture to fully aggregate multi-scale contextual information, resulting in a more complete spatial-spectral feature representation. Experiments on two benchmark datasets demonstrate that our approach achieves state-of-the-art classification performance.