[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.