MACHINE LEARNING CLUSTERING FOR POINT CLOUDS OPTIMISATION VIA FEATURE ANALYSIS IN CULTURAL HERITAGE

Abstract

The paper presents an innovative approach that can assist survey methods by applying AI algorithms to improve the accuracy of point clouds generated from UAV images. Firstly, the work individually analyses several photogrammetric accuracy parameters, including reprojection error, angle of intersection between homologous points, number of cameras for single Tie Point calculation, verifying that a single parameter is not sufficient to filter noise from a photogrammetric point cloud. Therefore, some of the calculated parameters were analysed with the Self-Organizing Map (SOM) and a K-means, to check the impact of the precision parameters for reducing the noise associated with the definition of the 3D model. In the case study, in both machine learning clustering algorithms used, it was observed that the parameter that most influences noise in photogrammetric point clouds is the angle of intersection.