Aerial Scene Classification Using Hierarchical and Multi-Stage Swin Transformer Features

Aerial image classification remains challenging due to complex spatial structures, high intra-class variability, and the need to capture semantic information across multiple spatial scales. This study investigates the potential of the Swin Transformer architecture to address the limitations of conventional deep learning approaches, particularly their restricted ability to model long-range contextual dependencies in high-resolution aerial images. The analysis focuses on the contribution of hierarchical feature representations extracted from different stages of the Swin Transformer to classification performance. Experiments were conducted on the AID, UCM21, and NWPU-RESISC45 benchmark datasets using multiple model variants and varying training proportions. The results show that combining intermediate and high-level features with the classification head achieves competitive and generally improved classification performance compared to other configurations, emphasizing the importance of multi-scale semantic feature representation. Additional analyses reveal that input image size and data augmentation strategies influence performance, while attention maps are used as an auxiliary visualization tool to support qualitative interpretation by highlighting relevant spatial regions. Overall, the findings indicate that hierarchical attention mechanisms and multi-stage feature integration can provide an effective representation strategy for Transformer-based aerial image classification.