Interpretable Fine-Grained Aircraft Classification Network for Remote Sensing Image With Image Pair Interaction and Neural Tree

This paper proposes a novel interpretable framework for fine-grained aircraft classification in high-stakes remote sensing applications. Our approach addresses three key challenges: small inter-class variance, large intra-class variance, and the need for model interpretability. Specifically, our framework is built on the Swin Transformer (SwinT) backbone and includes three main modules. First, we present the Dynamic Attention Fusion Module (DAFM), which adaptively fuses multi-stage attention maps from the SwinT backbone. By leveraging a dispersion-based weighting mechanism, DAFM balances the contributions of coarse and fine-grained features, capturing both global structures and localized details. Second, we propose the Adaptive Image Pair Interaction Module (AIPI), which dynamically adjusts feature interaction strategies based on intra-class and inter-class similarity, effectively enhancing informative regions and improving robustness. To further optimize discriminative power, we incorporate an AIPI loss function that enforces intra-class consistency and inter-class separability. Finally, we develop a Binary Neural Tree Module (BNTM) to hierarchically select and propagate informative image patches, enhancing both feature refinement and interpretability through explicit path-based decision-making. Extensive experiments on benchmark datasets demonstrate that our framework significantly improves classification accuracy and interpretability, making it well-suited for applications requiring transparent and reliable decision-making. The Code can be found at https://github.com/StarmanGzx/BNTM.