An empirical analysis of deep learning methods for small object detection from satellite imagery

Xiaohui Yuan et al.

Expert Systems with Applications2026https://doi.org/10.1016/j.eswa.2025.131061article
AJG 1
Weight
0.44

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https://doi.org/https://doi.org/10.1016/j.eswa.2025.131061

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@article{xiaohui2026,
  title        = {{An empirical analysis of deep learning methods for small object detection from satellite imagery}},
  author       = {Xiaohui Yuan et al.},
  journal      = {Expert Systems with Applications},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1016/j.eswa.2025.131061},
}

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An empirical analysis of deep learning methods for small object detection from satellite imagery

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Evidence weight

0.44

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.32 × 0.4 = 0.13
M · momentum0.57 × 0.15 = 0.09
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.