Analysis of LightGlue Matching for Robust TIN-Based UAV Image Mosaicking

초록

Highlights What are the main findings? SIFT + LightGlue outperforms traditional methods in terms of the number and spatial distribution of tiepoints/triplets in UAV images. SIFT + LightGlue maintains reprojection accuracy while achieving greater robustness of TIN mosaics. What are the implications of the main findings? SIFT + LightGlue enables fast TIN-based mosaicking even in low-texture regions, producing continuous triangulations with fewer voids. Adopting SIFT + LightGlue reduces or removes TIN post-processing (e.g., pseudo-tiepoints), enabling faster, more reliable mosaics.Highlights What are the main findings? SIFT + LightGlue outperforms traditional methods in terms of the number and spatial distribution of tiepoints/triplets in UAV images. SIFT + LightGlue maintains reprojection accuracy while achieving greater robustness of TIN mosaics. What are the implications of the main findings? SIFT + LightGlue enables fast TIN-based mosaicking even in low-texture regions, producing continuous triangulations with fewer voids. Adopting SIFT + LightGlue reduces or removes TIN post-processing (e.g., pseudo-tiepoints), enabling faster, more reliable mosaics.Abstract Recent advances in UAV (Unmanned Aerial Vehicle)-based remote sensing have significantly enhanced the efficiency of monitoring and managing agricultural and forested areas. However, the low-altitude and narrow-field-of-view characteristics of UAVs make robust image mosaicking essential for generating large-area composites. A TIN (triangulated irregular network)-based mosaicking framework is herein proposed to address this challenge. A TIN-based mosaicking method constructs a TIN from extracted tiepoints and the sparse point clouds generated by bundle adjustment, enabling rapid mosaic generation. Its performance strongly depends on the quality of tiepoint extraction. Traditional matching combinations, such as SIFT with Brute-Force and SIFT with FLANN, have been widely used due to their robustness in texture-rich areas, yet they often struggle in homogeneous or repetitive-pattern regions, leading to insufficient tiepoints and reduced mosaic quality. More recently, deep learning-based methods such as LightGlue have emerged, offering strong matching capabilities, but their robustness under UAV conditions involving large rotational variations remains insufficiently validated. In this study, we applied the publicly available LightGlue matcher to a TIN-based UAV mosaicking pipeline and compared its performance with traditional approaches to determine the most effective tiepoint extraction strategy. The evaluation encompassed three major stages-tiepoint extraction, bundle adjustment, and mosaic generation-using UAV datasets acquired over diverse terrains, including agricultural fields and forested areas. Both qualitative and quantitative assessments were conducted to analyze tiepoint distribution, geometric adjustment accuracy, and mosaic completeness. The experimental results demonstrated that the hybrid combination of SIFT and LightGlue consistently achieved stable and reliable performance across all datasets. Compared with traditional matching methods, this combination detected a greater number of tiepoints with a more uniform spatial distribution while maintaining competitive reprojection accuracy. It also improved the continuity of the TIN structure in low-texture regions and reduced mosaic voids, effectively mitigating the limitations of conventional approaches. These results demonstrate that the integration of LightGlue enhances the robustness of TIN-based UAV mosaicking without compromising geometric accuracy. Furthermore, this study provides a practical improvement to the photogrammetric TIN-based UAV mosaicking pipeline by incorporating a LightGlue matching technique, enabling more stable and continuous mosaicking even in challenging low-texture environments.

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