NQ-Protonet: Noisy Query Prototypical Network for Few-Shot Remote Sensing Scene Classification

Few-shot remote sensing scene classification, which aims to recognize unseen classes given only a few labeled samples, is a challenge task due to the complex content contained in remote sensing scenes. In this end, we propose a noisy query prototypical network (NQ-ProtoNet), which uses query-mix module (QM) to produce extra query samples with inter-ference information for classification and thus implicitly enhance the feature learning ability of model. Our method alleviates the problem of large intraclass variances and inter-class similarity of remote sensing scenes to some extent, and the positive experimental results on UC Merced and NWPU data sets show that it outperforms several few-shot learning methods.