Establishing accurate correspondences between aerial and ground images is facing immense challenges because of the drastic viewpoint, illumination, and scale variations resulting from significant differences in viewing angles, shoot timing, and imaging mechanisms. To cope with these issues, we propose an effective aerial-to-ground feature matching method, named Viewpoint-invariant Deformable Feature Transformation (VDFT), which aims to comprehensively enhance the discrimination of local features by utilizing deformable convolutional network (DCN) and seed attention mechanism. Specifically, the proposed VDFT is constructed consisting of three pivotal modules: (1) a learnable deformable feature network is established by using DCN and Depthwise Separable Convolution (DSC) to obtain dynamic receptive fields, addressing local geometric deformations caused by viewpoint variation; (2) an improved joint detection and description strategy is presented through concurrently sharing the multi-level deformable feature representation to enhance the localization accuracy and representation capabilities of feature points; and (3) a seed attention matching module is built by introducing self- and cross- seed attention mechanisms to improve the performance and efficiency for aerial-to-ground feature matching. Finally, we conduct thorough experiments to verify the matching performance of our VDFT on five challenging aerial-to-ground datasets. Extensive experimental evaluations prove that our VDFT is more resistant to perspective distortion and drastic variations in viewpoint, illumination, and scale. It exhibits satisfactory matching performance and outperforms the current state-of-the-art (SOTA) methods in terms of robustness and accuracy.

中文翻译：

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VDFT：使用视点不变的可变形特征变换对航空和地面图像进行鲁棒特征匹配


由于视角、拍摄时机和成像机制的显着差异导致视点、照明和比例变化剧烈，在航空图像和地面图像之间建立准确的对应关系面临着巨大的挑战。为了解决这些问题，我们提出了一种有效的空地特征匹配方法，称为视点不变可变形特征变换（VDFT），旨在利用可变形卷积网络（DCN）和种子来全面增强局部特征的辨别力注意机制。具体来说，所提出的VDFT由三个关键模块组成：（1）利用DCN和深度可分离卷积（DSC）建立可学习的可变形特征网络以获得动态感受野，解决视点变化引起的局部几何变形； (2)通过同时共享多级可变形特征表示，提出改进的联合检测和描述策略，提高特征点的定位精度和表示能力； (3)通过引入自种子注意力机制和交叉种子注意力机制构建种子注意力匹配模块，以提高空地特征匹配的性能和效率。最后，我们进行了彻底的实验，以验证 VDFT 在五个具有挑战性的空对地数据集上的匹配性能。大量的实验评估证明，我们的 VDFT 更能抵抗透视失真以及视点、照明和比例的剧烈变化。它表现出令人满意的匹配性能，并且在鲁棒性和准确性方面优于当前最先进的（SOTA）方法。