Open AccessMethod of Defining the Parameters for UAV Point Cloud Classification Algorithm
Author(s) -
Bui Ngoc Quy,
Le Dinh Hien,
Nguyễn Quốc Long,
Tong Si Son,
Duong Anh Quan,
Pham Van Hiep,
Phan Thanh Hải,
Phạm Thị Làn
Publication year - 2020
Publication title -
inżynieria mineralna
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.215
H-Index - 9
ISSN - 1640-4920
DOI - 10.29227/im-2020-02-08
Subject(s) - point cloud , computer science , cloud computing , process (computing) , drone , point (geometry) , volume (thermodynamics) , remote sensing , computer vision , algorithm , data mining , artificial intelligence , geography , mathematics , geometry , genetics , physics , quantum mechanics , biology , operating system
Image data from Drones/Unmanned Aerial Vehicles (UAVs) has been studied and used extensively for establishing maps. The process of UAV data provides three main products including (Digital Surface Model) DSM, Point cloud and Ortho-photos, in which point cloud is a valuable data source in building 3D models and topographic surfaces as well. However, processing point cloud separately to achieve secondary products has not been received much attention from researchers. This study determines parameters to develop a method for classifying point cloud data constructed from UAV images. Consequently, A 3D surface of the ground is built by applying a developed algorithm for the point cloud data for an open-pit mine. The temporal or non-ground objects such as trees, houses, vehicles are automatically subtracted from the point cloud by the algorithms. According to this line, it is possible to calculate and analyze the amount of reserves, the exploited volume to evaluate the efficiency for each mine during operation with the support of UAV integrated camera.