To solve the problem of scene classification performance caused by complex background， intra-class diversity and inter-class similarity in remote sensing scene images， a new remote sensing scene classification method based on supervised contrast learning is proposed. The method involves two stages： discriminative feature learning and linear classification. In the stage of discriminative feature learning， a supervised contrastive loss first is introduced to narrow the distance between similar scenes and increase the distance between different types of scenes， so as to improve the scene discriminative ability of intra-class diversity and inter-class similarity； secondly， a gated self-attention module is introduced to filter useless background information and focus on key scene parts for improving scene recognition capabilities with complex backgrounds； finally， the pre-trained Inception V3 branch is introduced， and the branch features are merged with the final features extracted by the original model to further enhance the feature discriminative ability for improving the overall performance of scene classification. In the linear classification stage， the classification results are obtained by fine-tuning the model trained in the first stage. Comprehensive experiments on AID and NWPU-RESISC45 datasets demonstrate the effectiveness of the proposed method.