Transformer-Based Cross-Domain Few-Shot Learning for Hyperspectral Target Detection

Deep learning-based methods have made significant progress in hyperspectral target detection (HTD). Unfortunately, limited target prior information and imbalance class resulting from the low occurrence probability of target leaves deep learning-based methods to confront bottlenecks. To ameliorate the abovementioned issues, a Transformer-based cross-domain few-shot learning (TCFSL) method is proposed for HTD. First, the TCFSL leverages cross-domain few-shot learning (FSL) to establish FSL tasks in both the source domain (SD) and the target domain (TD). This allows the TCFSL to learn transferable knowledge of the SD and distinguishable feature embedding model for the TD, to address the problems of target priori lacking and imbalance class. Second, feature-level and distribution-level domain adaptation (DA) is used to tackle the problem of domain shift in cross-domain FSL. The feature-level DA extracts intradomain information of the SD and TD to learn their common features to alleviate domain shift. The distribution-level DA based on cross-Transformer present interdomain distribution-level information aggregation and captures domain similarities of two data domains. By pursuing similarities between two data domains, the distribution-level DA block prompts specific FSL tasks in each domain, facilitating the target detection task. Finally, cross-domain FSL and DA blocks are trained in a unitary manner, which facilitates real-time information interaction and parameter adjustment between different blocks to achieve the optimal model. Experiments conducted on six HSI datasets indicate that the TCFSL outperforms 12 compared methods.