Diversity Consistency Learning for Remote Sensing Object Recognition with Limited Labels

Wenda Zhao

Abstract:

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 paper proposes a diversity consistency
learning for remote sensing object recognition with limited labels.
Specifically, diversity generation model is designed as a teacher
model to generate diverse results, which is trained with labeled
samples. Then, round consistency distillation model 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 diversity
generation model, 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.