ETQ-Matcher: Efficient Quadtree-Attention-Guided Transformer for Detector-Free Aerial–Ground Image Matching

UAV aerial–ground feature matching is used for remote sensing applications, such as urban mapping, disaster management, and surveillance. However, current semi-dense detectors are sparse and inadequate for comprehensively addressing problems like scale variations from inherent viewpoint differences, occlusions, illumination changes, and repeated textures. To address these issues, we propose an efficient quadtree-attention-guided transformer (ETQ-Matcher) based on efficient LoFTR, which integrates the multi-layer transformer with channel attention (MTCA) to capture global features. Specifically, to tackle various complex urban building scenarios, we propose quadtree-attention feature fusion (QAFF), which implements alternating self- and cross-attention operations to capture the context of global images and establish correlations between image pairs. We collect 12 pairs of UAV remote sensing images using drones and handheld devices, and we further utilize representative multi-source remote sensing images along with MegaDepth datasets to demonstrate their strong generalization ability. We compare ETQ-Matcher to classic algorithms, and our experimental results demonstrate its superior performance in challenging aerial–ground urban scenes and multi-source remote sensing scenarios.