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DMFNet introduces a dual-backbone architecture designed to improve remote sensing scene classification by fusing multiscale features through residual propagation and spatial attention mechanisms. This approach addresses common challenges in aerial imagery, such as high intra-class variability, achieving state-of-the-art accuracy on the AID benchmark dataset. The work represents significant progress in computer vision applications for AI-driven geospatial analysis rather than general consumer tech or networking standards.
DMFNet: Dual-Backbone Multiscale Fusion Network for Urban Scene Classification
arXiv:2607.16338v1 Announce Type: cross 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.
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.
Under review at InGARSS 2026. This work was conducted during the first author's Master's studies at Jadavpur University
Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
arXiv:2607.16338 [cs.CV]
(or arXiv:2607.16338v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2607.16338 Focus to learn more arXiv-issued DOI via DataCite
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