UNCERTAINTY-AWARE DYNAMIC LEARNING FOR CROSS-DOMAIN FEW-SHOT SCENE CLASSIFICATION FROM REMOTE SENSING IMAGERY

Cross-domain few-shot scene classification (CDFSSC) is devoted to transferring knowledge from the source domain to the target domain and facilitating few-shot classification for the target domain. However, due to the domain shifts between source and target domains, high uncertainty would be generated in the knowledge transfer process, leading to unreliable cross-domain learning, which degenerates classification performance on the target domain severely. Thus, in this paper, aiming to reduce the interference of high uncertainty and improve the reliability of cross-domain knowledge transfer, a novel uncertainty-aware dynamic learning (UDL) framework is proposed for CDFSSC from remote sensing imagery. First, a mean-teacher architecture combining pseudo-labeling and consistency regularization is utilized to achieve cross-domain learning. Second, a UDL strategy is proposed to divide data into positive and negative samples based on a well-designed uncertainty-aware dynamic threshold, conducting positive and negative learning respectively, to advance a more reliable knowledge transfer. Third, to further improve cross-domain capability, a self-entropy loss is designed to reduce the epistemic uncertainty of the model. Extensive experiment results indicate the superiority of our proposed methods.