【目的】特征匹配是航空影像三维重建的核心步骤之一。然而，航空影像在成像过程中受阴影、视角变化等因素的影响导致匹配点数量少且分布不均匀。【方法】本文提出了一种顾及阴影和视角差异的多策略融合特征匹配方法，该方法结合了传统的SIFT特征提取算法和前沿的LightGlue特征匹配学习网络，通过引入多种优化策略，实现了复杂成像条件下的高质量匹配效果，主要包括以下3点改进： ① 提出了一种自适应阴影区域增强策略，通过影像原始信息提取阴影区域，利用阴影区和非阴影区平均亮度之比确定初始亮度增强因子，并根据阴影区域间的灰度差异对亮度增强因子进行改正，实现阴影区域的亮度增强处理，以恢复阴影区域的地物细节信息，提升特征点数量； ② 引入多视角模拟影像生成策略，利用相机姿态构建多视角模拟影像，以提升输入特征的视角变化适应性，提高匹配质量； ③ 设计了一种基于K-Means聚类的RANSAC匹配优化算法，根据影像的原始色彩信息动态确定聚类数K，并剔除明显色彩不一致的匹配点，利用确定的K值对匹配点进行聚类处理并对每一簇分别利用RANSAC算法进行局部优化，降低平面假设误差，获取相应的内点作为最终匹配结果。【结果】实验基于A3相机采集的航空影像数据，对影像进行单一策略和组合策略处理。实验结果表明，经过自适应阴影区域增强策略和多视角模拟策略处理后，匹配点数量相比于未处理增加了近3倍，聚类RANSAC优化策略相比于传统RANSAC优化方法的平均像素距离误差降低了约30%，匹配正确率平均提升24.8%。【结论】本文方法有效地解决了复杂成像条件下航空影像匹配困难的问题，为三维重建等下游任务提供了更加充分且可靠的数据支持。

[Objectives] Feature matching is a core step in the 3D reconstruction of aerial images. However, due to shadows and perspective variations during the imaging process, the number of matching points is often small and unevenly distributed, significantly affecting accuracy. [Methods] This paper proposes a multi-strategy fusion feature matching method that accounts for shadow and viewing angle differences. It combines the traditional SIFT feature extraction algorithm with the advanced LightGlue feature matching neural network. Through multiple optimization strategies, the method achieves high-quality matching results under complex imaging conditions. The main improvements include the following: (1) An adaptive shadow region enhancement strategy is proposed. Shadow regions are extracted from the original image, and an initial brightness enhancement factor is determined based on the average brightness ratio of shadow and non-shadow areas. This factor is then adjusted using the gray-level differences within the shadow regions to enhance their brightness and restore ground object details, increasing the number of feature points. (2) A multi-view simulated image generation strategy is introduced. Simulated images are generated based on camera pose information to improve the adaptability of input features to view changes, enhancing matching accuracy and robustness. (3) In the matching optimization stage, due to significant height differences in aerial images, using a planar assumption for estimation introduces large errors. To address this, A RANSAC matching optimization algorithm based on K-Means clustering is developed. The number of clusters (K) is dynamically determined using the image's original color information. Matching points are clustered accordingly, and the RANSAC algorithm is applied to each cluster for local optimization. This reduces planar assumption errors and improves the selection of inliers. [Results] Experiments were conducted using aerial image data captured by the A3 camera, testing both single and combined strategies. Results show that after applying the adaptive shadow region enhancement and multi-view simulation strategies, the number of matching points nearly tripled compared to the unprocessed data. Additionally, after K-Means clustering RANSAC optimization, the average pixel distance error decreased by approximately 30% compared to direct RANSAC optimization, and the matching accuracy improved by an average of 24.8%. [Conclusions] The proposed method effectively addresses the challenges of aerial image matching under complex imaging conditions, providing more robust and reliable data support for downstream tasks such as 3D reconstruction.

利益冲突：Conflicts of Interest 所有作者声明不存在利益冲突。

All authors disclose no relevant conflicts of interest.