SAT-MAK: Digital Surface Model Generation from Satellite Imagery Using Multi-Type Aggregated Keypoints and Weighted Clustering

The generation of Digital Surface Models (DSMs) from large-format, high-resolution satellite imagery constitutes a critical component of photogrammetry and computer vision. Achieving efficient, robust, and high-quality DSM reconstruction has therefore become a prominent research focus. However, with the continuous improvement in satellite image resolution and the increasing diversity of image sources, satellite image matching—serving as the fundamental step in DSM generation—still faces significant challenges, including the uneven distribution of feature points and insufficient registration stability in large-scale imagery. To address these issues, this paper presents a refined DSM generation method for high-resolution satellite imagery, termed SAT-MAK. The framework consists of three main stages: (1) sparse matching based on MAK (Multi-type Aggregated Keypoints) extraction; (2) a density-weighted clustering matching optimization strategy; and (3) DSM generation following a conventional photogrammetric pipeline. Experiments were conducted on multiple sets of high-resolution satellite imagery, and the proposed method was compared with four commonly used satellite image 3D reconstruction algorithms. The results demonstrate that, compared with state-of-the-art methods, the proposed SAT-MAK approach improves DSM completeness by 5.29% while maintaining competitive RMSE performance, highlighting its strong potential for practical applications.