The acquisition of sufficient labeled samples is often a significant challenge in the field of remote sensing imagery, due to the time-consuming and high cost nature of field data collection. As a result, researchers have recently aimed to explore and develop effective few-shot learning methods that can overcome the shortage of labeled data in remote sensing imagery. Few-shot learning aims to enable machine learning algorithms to learn from a few labeled data or even from a single sample. The classification of remote sensing data is challenging because of the high interclass similarity and intraclass diversity found within remote sensing scenes. Direct computation of similarities between query and support data in current methods can lead to confusion. We propose a discriminative representation-based classifier (DRC) for few-shot remote sensing scene categorization to overcome this issue. Specifically, we introduce two discriminative constraint terms in the objective function: intraclass and interclass constraints. The intraclass constraint term enhances the concentration of the learned representation vectors in same class learned by the classifier, while the interclass constraint term reduces the correlation between the representation vectors of different categories. The experimental findings on the difficult remote sensing datasets NWPU-RESISC45 and RSD46-WHU demonstrate that our proposed DRC method delivers cutting-edge results in few-shot remote sensing scene image classification.