3D CHANGE DETECTION OF POINT CLOUDS BASED ON DENSITY ADAPTIVE LOCAL EUCLIDEAN DISTANCE

With the development of sensors and multi-view stereo matching technology, image-based dense matching point cloud data shares higher geometric accuracy and richer spectral information, and such data is therefore widely used in change detection-related research. Due to the inconsistent position and attitude of the image acquisition for generating two phases of point clouds, as well as the seasonal variation of vegetation, the 3D change detection is often subject to false detection. To improve the accuracy of 3D change detection of point clouds in large fields, a method of 3D change detection of point clouds based on density adaptive local Euclidean distance is proposed. The method consists of three steps: (1) Calculating the local Euclidean distances from each point in the second phase of point clouds to the k nearest neighboring points of the first phase of point clouds; (2) Improving the local geometric Euclidean distance based on the local density and performing 3D change detection according to a given threshold; (3) Clustering the change detection results using Euclidean clustering, and then eliminating the false detection area according to the given threshold. The experiments show that the changed region can be better extracted by the proposed method.