Mix-Mask Augmentation and Self-Reconstruction for Cross-Domain Few-Shot Hyperspectral Image Classification

Recently, the metric-based prototypical methods achieves promising performance in few-shot learning (FSL) for hyperspectral image (HSI) classification. However, the existing models are easily affected by the noisy pixels of different categories around the center pixel of the patch, and tend to focus on the most representative features while ignoring other important ones, which cause the overfitting problem. Moreover, the commonly used dimension reduction operation of the feature of source and target domains inevitably results in the loss of valuable spectral information. To address these issues, we propose the mix-mask augmentation and self-reconstruction for cross-domain HSI classification. The pixel mask augmentation is introduced to enhance the sample diversity of query set and suppress the impact of noisy pixels, thus encouraging the model to discover discriminative features on a wider range. The CutMix augmentation is also adopted to generate the mixed support set and mixed prototypes, mitigating the negative impact of confusing prototypes. Furthermore, we develop the self-reconstruction module which can preserve more useful feature information during the dimension reduction for feature representation of the source and target domains. Extensive experiments on three public HSI datasets demonstrate that the proposed method achieves superior performance with fewer computational costs in comparison with the SOTA methods.