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.
This article using some state-of-art multi-view dense matching methods for reference, proposes an UAV multiple image dense matching algorithm base on Self-Adaptive patch (UAV-AP) in view of the specialty of UAV images. The main idea of matching propagating based on Self-Adaptive patch is to build patches centered by seed points which are already matched. The extent and figure of the patches can adapt to the terrain relief automatically: when the surface is smooth, the extent of the patch would become bigger to cover the whole smooth terrain; while the terrain is very rough, the extent of the patch would become smaller to describe the details of the surface. With this approach, the UAV image sequences and the given or previously triangulated orientation elements are taken as inputs. The main processing procedures are as follows: (1) multi-view initial feature matching, (2) matching propagating based on Self-Adaptive patch, (3) filtering the erroneous matching points. Finally, the algorithm outputs a dense colored point cloud. Experiments indicate that this method surpassed the existing related algorithm in efficiency and the matching precision is also quite ideal.
The robust and rapid matching of oblique UAV images of urban area remains a challenge until today. The method
proposed in this paper, Nicer Affine Invariant Feature (NAIF), calculates the image view of an oblique image by making
full use of the rough Exterior Orientation (EO) elements of the image, then recovers the oblique image to a rectified
image by doing the inverse affine transform, and left over by the SIFT method. The significance test and the left-right
validation have applied to the matching process to reduce the rate of mismatching. Experiments conducted on oblique
UAV images of urban area demonstrate that NAIF takes about the same time as SIFT to match a pair of oblique images
with a plenty of corresponding points and an extremely low mismatching rate. The new algorithm is a good choice for
oblique UAV images considering the efficiency and effectiveness.