Target localization plays a key role in unmanned aerial vehicle (UAV) reconnaissance systems. Map-based target localization methods offer the advantage of being unaffected by installation and measurement errors. However, images captured by UAVs in complex ground environments often contain high-density feature points. Moreover, variations in illumination and cross-view between the target and reference images frequently introduce significant noise. These factors make it challenging for traditional map-based target localization methods to accurately identify and eliminate incorrect feature pair matches. Particularly under constrained spatial conditions, traditional map-based target localization methods such as Random Sample Consensus often fail to fully leverage geographical information and geometric relationships, hindering the accurate filtering of outliers. The above issue can lead to incorrect positional information, thereby reducing target localization accuracy. To address these challenges, we introduce the Local Affine Consistency Guided Triplet Sampling Consensus (LATSAC) algorithm, which is based on triplet feature matching. Benefit from a triplet-stable relationship, this method effectively filters outliers during UAV reconnaissance target localization missions in complex environments. To enhance the efficiency of the proposed algorithm in UAV reconnaissance missions, an improved initial data selection strategy is designed. In addition, to increase the target localization accuracy, a combined approach integrating a compensation term strategy with a data subset refinement strategy is developed. Experimental results from five challenging scenarios demonstrate that LATSAC achieves the lowest computational cost compared with that of existing state-of-the-art techniques for UAV reconnaissance target localization missions in complex environments. In addition, LATSAC exhibits superior outlier filtering performance when processing a high density of outliers, significantly improving target localization accuracy.

In UAV visual localization applications, the quality of image matching directly affects both the precision and reliability of the visual localization task. In UAV visual localization tasks, high-resolution remote sensing images are typically used as reference maps, whereas UAV-acquired aerial images serve as real-time inputs, enabling the estimation of the UAV’s spatial position through image matching. However, due to the substantial difference in imaging mechanisms and acquisition conditions between reference and real-time images, heterogeneous image pairs often contain numerous outliers, which significantly hinder the direct application of traditional matching algorithms such as RANSAC. To address these challenges, a spatial consistency-guided sampling algorithm is proposed. First, the initial correspondences are constructed based on triplet relationships, and their structural features are subsequently extracted. Then, a minimal subset sampling strategy is developed to improve sampling efficiency. Next, a data subset refinement strategy is introduced to further improve the robustness of sampling. Finally, extensive comparative experiments are conducted on the University-1652 and DenseUAV public datasets against several state-of-the-art feature matching algorithms. The experimental results demonstrate that the proposed algorithm achieves superior performance in correct matching rate, substantially enhancing the matching performance in heterogeneous image matching. Moreover, the proposed algorithm requires approximately 0.15 s per matching on average, and while maintaining the highest matching accuracy, it exhibits significantly higher computational efficiency than advanced sampling algorithms such as TRESAC and RANSAC, demonstrating strong potential for real-time applications in UAV visual localization tasks.

Image registration is fundamental to UAV remote sensing data processing, but repetitive textures, viewpoint and scale variations, terrain relief, and local deformation can introduce numerous outliers into tentative correspondences. The existing random-sampling and spatial-consistency methods commonly rely on pointwise confidence or isolated pairwise relations and therefore cannot reliably determine whether a locally compatible relation receives stable group-level structural support. To address this limitation, we propose SPGSC++, a second-order spatial guided sampling method for outlier removal. SPGSC++ first uses PCA to reduce the dimensionality of tentative correspondences, thereby reducing the computational burden of subsequent spatial relation construction. It then derives second-order structural quality from the common-neighbor support of directly compatible edges and uses this quality for initial data subset selection and residual-informed data subset refinement to generate and optimize a homography. Comparative experiments show that, on the five datasets CIAP, SUIRD, UAV, ZHANGZHOU, and PAN, the proposed method achieves mean Precision, Recall, F-Score, Runtime, and mean reprojection error values of 0.9627, 0.9797, 0.9702, 0.3724 s, and 1.6454, respectively. Relative to the original SPGSC, SPGSC++ increases the mean F-Score by 0.0391 and reduces Runtime and mean reprojection error by 32.39% and 58.12%, respectively. Overall, the innovation of SPGSC++ lies in using second-order common-neighbor structural quality to guide initial subset selection and residual-informed refinement for robust estimation in UAV remote sensing image registration.