Diversity Consistency Learning for Remote-Sensing Object Recognition With Limited Labels

Annotating remote-sensing object recognition needs high professionalism, and thus limited labeled samples are available. Suffering from this, general remote-sensing object recognition methods are facing low recognition accuracy. Addressing this issue, this article proposes a diversity consistency learning (DCL) for remote-sensing object recognition with limited labels. Specifically, the diversity generation model (DGM) is designed as a teacher model to generate diverse results, which is trained with labeled samples. Then, a round consistency distillation model (RCDM) is introduced to distill the knowledge of diverse pseudo-labels to a student network, which is trained with unlabeled samples. Especially, diverse pseudo-labels are generated by the well-trained DGM, which can improve recognition accuracy since diverse pseudo-label errors can cancel each other out. Extensive experiments on two widely used datasets of FS23 and HRSC2016 demonstrate the superior performance of our method compared with the state of the arts (SOTAs).