Uncertainty-Injected Cross-Domain Few-Shot Scene Classification From Remote Sensing Imagery

Cross-domain few-shot scene classification (CDFSSC) is crucial for remote sensing (RS) applications since it aims at transferring knowledge learned from the source domain to the target domain to facilitate the model's few-shot classification for the target domain. However, existing methods ignored the feature statistic discrepancy caused by domain shifts, leading to an inferior performance on the target domain. In this paper, to facilitate the model's adaptation of the domain shifts and achieve better cross-domain knowledge transfer, an uncertainty-injected cross-domain framework called UICD is proposed for CDFSSC tasks from RS imagery. First, a semi-supervised teacher-student structure is employed to achieve cross-domain knowledge transfer by conducting supervised learning on labeled source data and establishing consistent predictions on unlabeled target data. Secondly, uncertainty is injected in feature statistic modeling during cross-domain training to obtain more diverse feature statistics for data from both the source and target domains, which could promote the robustness and adaptation of the model to domain shifts, thus enabling the model to better adapt to unforeseen variations in the target domain. Extensive experiment results indicate the efficacy and superiority of the proposed methods.