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The scene matching and positioning of Unmanned aerial vehicles (UAVs) are prone to mismatching or even retrieval failure due to the differences in domain, observation angle and other factors between UAV images and satellite reference images. To address this issue, a rapid cross-source image retrieval method based on salient location features is proposed. Firstly, to solve the matching failure caused by scene and time differences between UAV images and reference images, a salient position feature extraction module is designed, which can extract more effective context information while reducing the computational complexity. Secondly, a label smoothing loss function is introduced to enhance the generalization ability of the model. Finally, a block-wise fine-tuning strategy is proposed to alleviate the overfitting problem of large models like vision transformer (ViT) under limited training data conditions. The experimental results show that the proposed method achieves 86.01% and 96.52% respectively in R@1 and R@5 on the DenseUAV dataset, and 76.04% in mAP, which is improved by 5.83%, 3.53% and 9.49% respectively compared with ViT-S. The retrieval time for a single image is 9.55 ms on the DenseUAV dataset, indicating the effectiveness of the proposed method in UAV cross-source scene matching.
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| 科 Family | 属数 Number of genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) | 属 Genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) |
|---|---|---|---|---|---|---|
| 鹅膏菌科Amanitaceae | 2 | 11 | 5.26 | 鹅膏菌属 Amanita | 10 | 4.78 |
| 小菇科 Mycenaceae | 2 | 12 | 5.74 | 丝盖伞属 Inocybe | 5 | 2.39 |
| 多孔菌科 Polyporaceae | 8 | 14 | 6.70 | 蜡蘑属 Laccaria | 5 | 2.39 |
| 红菇科 Russulaceae | 3 | 23 | 11.00 | 小皮伞属 Marasmius | 6 | 2.87 |
| 小菇属 Mycena | 11 | 5.26 | ||||
| 光柄菇属 Pluteus | 5 | 2.39 | ||||
| 红菇属 Russula | 17 | 8.13 | ||||
| 栓菌属 Trametes | 5 | 2.39 |
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