In response to the challenges of scene scale variability, intra-class diversity, and inter-class similarity in remote sensing image scene classification, a Vision Transformer (ViT) model that integrates multi-scale features with detail perception strategies is proposed for remote sensing image classification. The model effectively captures and fuses multi-scale features from remote sensing images by incorporating a dilated spatial pyramid pooling module, while enhancing the utilization of local feature information, thus improving feature discrimination capabilities. Furthermore, an innovative detail perception masking strategy enables the model to leverage unlabeled remote sensing image data effectively, facilitating the learning of more refined feature representations for more efficient and accurate scene classification. In the experimental section, the model is first pre-trained on a large-scale unlabeled remote sensing image dataset, followed by the fine-tuning of the pre-trained model on downstream scene classification tasks. Experimental results across multiple public remote sensing image datasets demonstrate that the proposed model can effectively extract image features during the self-supervised pre-training phase and achieve high accuracy in downstream scene classification tasks, showcasing robust performance and efficacy.