VDFT: Robust feature matching of aerial and ground images using viewpoint-invariant deformable feature transformation

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.