Heterogeneous high-resolution remote sensing image matching will be disturbed by the differences in sensor type, imaging angle, height, and imaging time, and the matching difficulty is further increased in complex scenes with dense urban buildings and noticeable height differences. This paper proposes a method for matching heterogeneous high-resolution remote sensing images based on partitioned feature extraction and three-dimensional spatial constraints. First, this paper conducts image partitioning based on the geometric differences of ground objects. Two feature extraction methods, namely, adaptive phase threshold and weighted moment map, are employed to extract feature points independently. To address the issue of inaccurate feature descriptions caused by drastic changes in viewing angles in buildings, we construct a robust feature descriptor by combining a multi-scale phase weighted energy convolution histogram (MSPW-ECH) with a new gradient location orientation histogram (GLOH)-like local feature descriptor. Additionally, a new similarity measure incorporating three-dimensional spatial constraints and the Marginalizing Sample Consensus (MAGSAC) method is applied to eliminate mismatched point pairs, ensuring the acquisition of precise matching points. Based on the feature detection results of two different synthetic data sets, it is evident that the proposed detector outperforms the three classical detectors in terms of repeatability and uniformity. Ultimately, the matching performance is experimentally verified on six groups of heterogeneous high-resolution remote sensing images. The experimental results show that the proposed method significantly outperforms RIFT, HAPCG, and MS-HLMO methods and achieves the best matching accuracy results.

Building height is a critical variable in urban studies, and the automated acquisition of the precise building height is essential for intelligent construction, safety, and the sustainable development of cities. The building height is often approximated by the building’s highest point. However, the calculation method of the building height of the various roof types differs according to building codes, making it challenging to accurately calculate the height of buildings with complex roof structures or multiple upper appendages. Consequently, this paper utilizes point clouds to propose an automated method for calculating building heights conforming to design codes. The model considers roof types and allows for fast, automated, and highly accurate building height estimation. First, roofs are extracted from the point cloud by combining normal vector density clustering with a region-growing algorithm. Second, combined with variational Bayes, a Gaussian mixture model is employed to segment the roof surfaces. Finally, roofs are classified based on slope characteristics, achieving the automatic acquisition of building heights for various roof types over large areas. Experiments were conducted on Vaihingen and STPLS3D datasets. In the Vaihingen area, the maximum error, root-mean-square-error (RMSE), and mean absolute error (MAE) of the measured heights are 1.92 cm, 1.18 cm, and 1.13 cm, respectively. In the STPLS3D area, these values are 1.79 cm, 0.82 cm, and 0.68 cm, respectively. The results demonstrate that the proposed method is reliable and effective, which offers valuable data for the development, construction, and planning of three-dimensional (3D) cities.