This paper presents a novel multi-view dense point cloud generation algorithm based on low-altitude remote sensing images. The proposed method was designed to be especially effective in enhancing the density of point clouds generated by Multi-View Stereo (MVS) algorithms. To overcome the limitations of MVS and dense matching algorithms, an expanded patch was set up for each point in the point cloud. Then, a patch-based Multiphoto Geometrically Constrained Matching (MPGC) was employed to optimize points on the patch based on least square adjustment, the space geometry relationship, and epipolar line constraint. The major advantages of this approach are twofold: (1) compared with the MVS method, the proposed algorithm can achieve denser three-dimensional (3D) point cloud data; and (2) compared with the epipolar-based dense matching method, the proposed method utilizes redundant measurements to weaken the influence of occlusion and noise on matching results. Comparison studies and experimental results have validated the accuracy of the proposed algorithm in low-altitude remote sensing image dense point cloud generation.

Robust and rapid image dense matching is the key to large-scale three-dimensional (3D) reconstruction for multiple Unmanned Aerial Vehicle (UAV) images. However, the following problems must be addressed: (1) the amount of UAV image data is very large, but ordinary computer memory is limited; (2) the patch-based multi-view stereo-matching algorithm (PMVS) does not work well for narrow-baseline cases, and its computing efficiency is relatively low, and thus, it is difficult to meet the UAV photogrammetry’s requirements of convenience and speed. This paper proposes an Image-grouping and Self-Adaptive Patch-based Multi-View Stereo-matching algorithm (IG-SAPMVS) for multiple UAV imagery. First, multiple UAV images were grouped reasonably by a certain grouping strategy. Second, image dense matching was performed in each group and included three processes. (1) Initial feature-matching consists of two steps: The first was feature point detection and matching, which made some improvements to PMVS, according to the characteristics of UAV imagery. The second was edge point detection and matching, which aimed to control matching propagation during the expansion process; (2) The second process was matching propagation based on the self-adaptive patch. Initial patches were built that were centered by the obtained 3D seed points, and these were repeatedly expanded. The patches were prevented from crossing the discontinuous terrain by using the edge constraint, and the extent size and shape of the patches could automatically adapt to the terrain relief; (3) The third process was filtering the erroneous matching points. Taken the overlap problem between each group of 3D dense point clouds into account, the matching results were merged into a whole. Experiments conducted on three sets of typical UAV images with different texture features demonstrate that the proposed algorithm can address a large amount of UAV image data almost without computer memory restrictions, and the processing efficiency is significantly better than that of the PMVS algorithm and the matching accuracy is equal to that of the state-of-the-art PMVS algorithm.

This paper presents a point cloud optimization method of low-altitude remote sensing image based on least square matching (LSM). The proposed method is designed to be especially effective for addressing the conundrum of stereo matching on the discontinuity of architectural structures. To overcome the error matching and blur on building discontinuities in three-dimensional (3-D) reconstruction, a pair of mutually perpendicular patches is set up for every point of object discontinuities instead of a single patch. Then an error equation is built to compute the optimal point according to the LSM method, space geometry relationship, and collinear equation constraint. Compared with the traditional patch-based LSM method, the proposed method can achieve higher accuracy 3-D point cloud data and sharpen the edge. This is because a geometric mean patch in patch-based LSM is the local tangent plane of an object’s surface. Using a pair of mutually perpendicular patches instead of a single patch evades the problem that the local tangent plane on the discontinuity of a building did not exist and highlights the edges of buildings. Comparison studies and experimental results prove the high accuracy of the proposed algorithm in low-altitude remote sensing image point cloud optimization.

Digital Elevation Model (DEM)-based mountain vertex extraction is one of the most useful DEM applications, providing important information to properly characterize topographic features. Current vertex-extraction techniques have considerable limitations, such as yielding low-accuracy results and generating false mountain vertices. To overcome these limitations, a new approach is proposed that combines Hotspot Analysis Clustering and the Improved Eight-Connected Extraction algorithms that would quickly and accurately provide the location and elevation of mountain vertices. The use of the elevation-based Hotspot Analysis Clustering Algorithm allows the fast partitioning of the mountain vertex area, which significantly reduces data and considerably improves the efficiency of mountain vertex extraction. The algorithm also minimizes false mountain vertices, which can be problematic in valleys, ridges, and other rugged terrains. The Eight-Connected Extraction Algorithm also hastens the precise determination of vertex location and elevation, providing a better balance between accuracy and efficiency in vertex extraction. The proposed approach was used and tested on seven different datasets and was compared against traditional vertex extraction methods. The results of the quantitative evaluation show that the proposed approach yielded higher efficiency, considerably minimized the occurrence of invalid points, and generated higher vertex extraction accuracy compared to other traditional methods.

This article presents a novel matching optimization algorithm for low-altitude remote sensing images based on a geometrical constraint and a convolutional neural network (CNN). The proposed method was designed to be effective in enhancing the integrity and accuracy of point clouds generated by stereo matching. To overcome the limitations of stereo matching, we trained a CNN to predict how well image patches match and used it in patch optimization. The main advantage of this approach is that the proposed algorithm can decrease the mismatching and errors caused by noise, deep discontinuity, and weak texture in low-altitude remote sensing images and can reconstruct an integrated and accurate point cloud. Comparative studies and experimental results validate the accuracy of the proposed algorithm when used for dense point generation from low-altitude remote sensing images.

This paper presents a robust point cloud optimization algorithm based on nonadjacent low-altitude remote sensing images. The proposed algorithm is designed to optimize the performance of point cloud generation in accuracy and efficiency. In order to accelerate the process of stereo matching and compute the coordinate of object point accurately, a pair of nonadjacent images is employed to lengthen the epipolar line of the image pair. Then a patch based Least Square Matching (LSM) method is utilized to search the optimal matching pixels and compute the coordinates of corresponding object points in 3D space. Comparison studies and experimental results in point cloud generation about low altitude remote sensing images have proved the effectiveness of the proposed algorithm.