DMFNet: Dual-Backbone Multiscale Fusion Network for Urban Scene Classification

2026-07-16

AI Summary

This work addresses the challenges of insufficient multi-scale feature interaction and poor representation robustness in remote sensing urban scene classification, which arise from large intra-class variations and high inter-class similarities. To this end, the authors propose a dual-backbone multi-scale fusion architecture that enhances cross-scale feature interaction through a residual feature propagation mechanism and incorporates a spatial attention module to emphasize discriminative regions. A two-stage freeze-and-fine-tune training strategy is further introduced to improve model generalization. Evaluated on the AID dataset, the proposed method achieves an average accuracy of 97.46% ± 0.14%, and ablation studies confirm the effectiveness of each component.

Abstract

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.

Problem

• remote sensing scene classification
• multiscale feature interaction
• intra-class variability
• inter-class similarity
• feature representation

Innovation

• dual-backbone
• multiscale fusion
• residual feature propagation
• spatial attention
• two-stage training