发表机构
Jadavpur University（贾达沃布尔大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对遥感场景分类中捕捉多尺度特征交互及学习鲁棒特征表示的挑战，DMFNet采用双骨干网络，通过残差特征传播融合多尺度特征，引入空间注意力模块，并采用两阶段训练策略，在AID数据集上取得高准确率，证明组件协同重要性。
AI 中文摘要
本文提出了DMFNet，一种用于遥感场景分类的双骨干多尺度特征融合框架，具有残差特征传播和空间注意力机制。现有方法在有效捕捉多尺度特征交互以及从具有高类内变异性和类间相似性的复杂航空场景中学习鲁棒特征表示方面常面临挑战。为解决这些限制，该框架采用两个预训练骨干网络提取不同层次特征表示，引入残差特征传播的多尺度特征融合机制增强跨分辨率特征交互，还引入空间注意力模块强调多目标场景中的信息空间区域。此外，采用先冻结骨干网络再进行选择性微调的两阶段训练策略确保稳定优化和更好的泛化能力。在基准AID数据集上的实验表明，DMFNet平均准确率达97.46%±0.14%，消融分析进一步显示了各组件协同的重要性。
英文摘要
This article presents DMFNet, a dual-backbone multiscale feature fusion framework with residual feature propagation and spatial attention for remote sensing scene classification. Existing approaches often face challenges in effectively capturing multiscale feature interactions and learning robust feature representations from complex aerial scenes with high intra-class variability and inter-class similarity. To address these limitations, the proposed framework employs two pretrained backbone networks to extract diverse hierarchical feature representations. A multiscale feature fusion mechanism with residual feature propagation is introduced to enhance feature interaction across multiple resolution levels. In addition, a spatial attention module is introduced to emphasize informative spatial regions in multi-object scenes. Further, a two-stage training strategy consisting of backbone freezing followed by selective fine-tuning is adopted to ensure stable optimization and improved generalization. Experiments conducted on the benchmark AID dataset demonstrate that the DMFNet achieves an average accuracy of 97.46\% $\pm$ 0.14\%. Ablative analysis further show the importance of various components in unison.
CommentsUnder review at InGARSS 2026. This work was conducted during the first author's Master's studies at Jadavpur University
