Feature point extraction and matching are crucial aspects of remote sensing image processing. Currently, most mature algorithms are designed for remote sensing images of Earth’s surface, with little consideration for the imaging conditions and the influence of the detection platform on spatial target images. As a result, the quality of feature point matching for spatial target images is often poor. To address the issue of low matching accuracy for spatial targets, this paper proposes a clustering-based feature point matching algorithm. First, feature points are extracted and described based on the repetitive weak textures of spatial targets. Then, clustering is performed using the spatial positions of the feature points, and matching is carried out for the clusters of feature points. Subsequently, the main direction of each feature point cluster is adjusted by subtracting the overall direction of the target. This adjustment is used to further group the points within each cluster, facilitating feature point matching. Finally, outliers are eliminated using the nearest neighbor-to-second-nearest-neighbor ratio method and the Random Sample Consensus algorithm (RANSAC). Simulation experiments with imaging data using this feature point matching method demonstrate that, for spatial target images, clustering-based feature point matching outperforms direct matching. The improvement in the number of matches can reach up to 50%, and the reprojection error is better than 1/4 pixel. The method proposed in this paper utilizes various commonly used feature descriptors, significantly enhancing the quantity and accuracy of feature point matching for spatial target images.