DOMAIN-SPECIFIC AND DOMAIN-COMMON FEATURE ENHANCEMENT FOR CROSS-DOMAIN FEW-SHOT HYPERSPECTRAL IMAGE CLASSIFICATION

There is a small sample problem in hyperspectral image (HSI) classification task due to the difficulty of labeling samples. It is generally solved using a combination of few-shot learning and cross-domain method. In the paper, we propose a domain-specific and domain-common feature enhancement method for cross-domain few-shot HSI classification. It consists of a domain adaptation module and a feature enhancement module. The former is used to learn domain-specific features of both domains from the beginning of the network, and the latter is used to reduce domain differences by learning domain-common features through feature enhancement. The experimental results indicate that our proposed method performs better than the advanced classification methods.