Airborne Light Detection And Ranging (LiDAR) point cloud is an important data source for building 3D digital cities and can be used to acquire and update urban buildings, roads and etc. However, it is difficult to extract complete and accurate building boundaries from airborne LiDAR point clouds due to partial occlusion, which is mainly caused by adjacent tall trees, particularly in spring and summer. In this paper, we propose an improved minimum bounding rectangle (IMBR) algorithm to extract complete and accurate regularized building boundaries with and without partial occlusion from aerial LiDAR point clouds. The new algorithm only uses LiDAR point cloud and doesn’t need any additional data source. In addition, the algorithm can be applied to buildings with complex shapes. To test the proposed algorithm and compare it with the recursive minimum bounding rectangle (RMBR) algorithm, three datasets with different types of partial occlusions and different shapes were tested. The experimental results show that IMBR can successfully extract the complete and accurate regularized building boundary with or without partial occlusion, and its accuracy is equal to that of RMBR algorithm.

A Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM)-aided geopositioning method is proposed to solve the problem of geopositioning without ground control points for Mapping Satellite-1 imagery. The method comprises coarse and accurate correction stages, and it compensates errors gradually. DEM extraction and DEM matching are important steps in both the stages, the objectives of which are to compensate the relative and absolute errors in an image, respectively. The SRTM DEM is integrated into all the processes to take full advantage of its consistent and high accuracy. Experimental results showed that this method could greatly improve geometry accuracy and obtain stable and highly accurate geopositioning for Mapping Satellite-1 images, regardless of the land area proportion (LAP) or the production mode. The planimetric and vertical accuracies were better than 8.1 and 5.2 m, respectively, which could satisfy the accuracy requirements of mapping at 1:50 000 scale. The computational efficiency depends on the LAP and target DEM resolution.

Photogrammetry based on high-resolution satellite image can acquire geospatial information within a large area rapidly and timely, but its geopositioning accuracy is highly dependent on ground control points. Under the background of global mapping, a public digital elevation model (DEM) assisted Chinese satellite image geopositioning scheme was proposed to realize satellite photogrammetry without ground control points. To make full use of public DEM advantages of consistent and high accuracy, public DEM was regarded as reference data and matched with the DEM extracted from image, and then the determined transformation parameters were applied to correct direct georeferencing results in object space. A fast least Z-difference method combined with least trimmed squares estimator was proposed to achieve DEM matching, which can not only automatically select corresponding point determination model, but also self-adaptively detect and eliminate difference between DEMs. Multi-groups of comparative experiments using Mapping Satellite-1 and ZiYuan-3 surveying satellite images were designed. Experimental results show that the DEM assisted geopositioning scheme exploits advantages of reference DEM, which can greatly improve the accuracy of photogrammetry without ground control points to a relatively high level. The geopositioning accuracy of image is largely determined by, but not confined to, the accuracy of reference DEM, but it is slightly affected by the resolution of reference DEM. If target DEM resolution is relatively high, the geopositioning accuracy of a single image assisted by Shuttle Radar Topography Mission (SRTM) DEM can satisfy the accuracy requirements of 1:50,000 scale mapping perfectly. This scheme also has good robustness and high computational efficiency.

Automatic DEM deformation detection without ground control points (GCPs) with its application in debris flow change analysis is an attractive but very difficult research topic. Based on the existing methods of M‐LZD and LMS‐LZD, this paper presents an improved method that takes both magnitude and relationship into account through allocating an appropriate weight to each observation and evaluating the contribution of each observation to deformation with the allocated weight. Such improvement enhances greatly the ability of DEM deformation detection. Compared with the M‐LZD and LMS‐LZD algorithm, using the multi‐temporal DEMs of the Puwaigou valley, along the Hengduan mountain ranges in the southwest of China, where debris flows occur frequently during each summer due to heavy rainfall and each Autumn due to rapid snowmelt, the experimental results demonstrate that our method has the highest detection accuracy (RMS) and detection percentage (capability) of over 50%. The advantages of our method are (1) it does not need GCPs and prior information and knowledge of the terrain surface, and (2) it can calculate simultaneously the volume and determine the spatial distribution of terrain surface changes of multi‐temporal DEMs.

A two-step vehicle recognition method from an aerial Lidar point cloud is proposed in this paper. First, the Lidar point cloud is segmented using the region-growing algorithm with vehicle size limitation. Then the vehicle is recognized according to the profile shape based on dynamic time warping. The proposed method can detect vehicles parking under trees in an urban scene, and classifies the vehicles into different classes. The vehicle location, orientation, parking direction and size can also be determined. The experimental result based on a real urban Lidar point cloud shows that the proposed method can correctly recognize 95.1 % of vehicles.