Cross-view image matching aims to match images of the same target scene acquired from different platforms. With the rapid development of drone technology, cross-view matching by neural network models has been a widely accepted choice for drone position or navigation. However, existing public datasets do not include images obtained by drones at different heights, and the types of scenes are relatively homogeneous, which yields issues in assessing a model's capability to adapt to complex and changing scenes. In this end, we present a new cross-view dataset called SUES-200 to address these issues. SUES-200 contains 24120 images acquired by the drone at four different heights and corresponding satellite view images of the same target scene. To the best of our knowledge, SUES-200 is the first public dataset that considers the differences generated in aerial photography captured by drones flying at different heights. In addition, we developed an evaluation for efficient training, testing and evaluation of cross-view matching models, under which we comprehensively analyze the performance of nine architectures. Then, we propose a robust baseline model for use with SUES-200. Experimental results show that SUES-200 can help the model to learn highly discriminative features of the height of the drone.
翻译:交叉视图图像匹配旨在匹配从不同平台获取的同一目标场景的图像。 随着无人机技术的快速发展,神经网络模型的交叉视图匹配已经成为无人机位置或导航方面广泛接受的选择。 但是,现有的公共数据集并不包括无人机在不同高度获得的图像,而场景的类型相对均匀,因此在评估模型适应复杂和变化场景的能力方面产生了问题。我们为此提出了一个新的交叉视图数据集,称为SUES-200,以解决这些问题。SUES-200包含由无人机在四个不同高度获得的24120图像以及同一目标场景的相应卫星图像。据我们所知,SUES-200是第一个考虑不同高度飞行的无人机所捕捉的航空摄影所产生的差异的公共数据集。此外,我们为高效培训、测试和评价交叉视图匹配模型开发了评估问题,据此我们全面分析了9个架构的性能。然后,我们提出了一个强大的基线模型,供SUES-200使用。实验结果显示,SUES-200可以帮助模型学习高度的高度。