Double Discriminative Constraints-based Affine Nonnegative Representation for Few-shot Remote Sensing Scene Classification

Remote sensing scene classification (RSSC) has recently attracted more attention. However, due to restrictions in the imaging environment and equipment, it is difficult to get a large number of labeled images in remote sensing. This has led to the emergence of few-shot learning for RSSC, which aims to achieve better performance with few labeled samples. Remote sensing images' large interclass similarity may cause classification confusion. To overcome this issue, this study proposes a double discriminative constraints-based affine nonnegative representation for few-shot RSSC. To be specific, we devise a novel representation-based classifier with two discriminative constraint terms in the objective function and utilize affine nonnegative constraints to restrict the learned parameters. These constraints reduce the correlation between classes and strengthen the class specificity of the learned parameters. Experiments on benchmark datasets demonstrate the effectiveness of our method.