Herein, a method of post-processing filtering based on space vector projection is proposed to eliminate the near-ground points that have a considerable impact on the accuracy of digital elevation model (DEM) construction to address the problem of difficulty in generating a high-quality DEM via single progressive morphological filtering for airborne light detection and airborne ranging (LiDAR) data. Initially, the proposed method takes each laser point as starting point and then constructs vectors with the lowest laser point of each of the nine grids closest to it at the end. Thereafter the method accumulates the projection of each of the nine vectors in the Z direction, compares the results to the preset threshold, and identifies as well as classifies the laser point. To validate the effectiveness of the proposed method, this study selects six groups of test data from the international society for photogrammetry and remote sensing (ISPRS) under different terrain conditions and generates 1 m × 1 m resolution DEM using ground points extracted before and after the post-processing filtering as well as performs linear fitting with reference DEM in the same research area. The results show that when compared with a single progressive morphological filtering algorithm, the combined algorithm of progressive morphological filtering and post-processing filtering based on space vector projection can achieve higher precision of point cloud filtering and DEM construction in urban area and rural area with continuous terrain as well as has good applicability and reliability.