THE SEGMENTATION OF POINT CLOUDS WITH K-MEANS AND ANN (ARTIFICAL NEURAL NETWORK)

• Published in: International archives of the photogrammetry, remote sensing and spatial information sciences. Vol. XLII-1/W1; pp. 595 - 598
• Main Authors: Kuçak, R. A., Özdemir, E., Erol, S.
• Format: Journal Article Conference Proceeding
• Language: English
• Published: Gottingen Copernicus GmbH 01.01.2017; Copernicus Publications
• Subjects: Airborne sensing; Artificial neural networks; Curvature; Detection; Furniture; Geodesy; Lasers; Lidar; Luminous intensity; Mathematical models; Methods; Neural networks; Photogrammetry; Remote sensing; Segmentation; Self organizing maps; Terrestrial environments; Three dimensional models; Urban areas
• ISSN: 2194-9034; 1682-1750; 2194-9034
• DOI: 10.5194/isprs-archives-XLII-1-W1-595-2017

Segmentation of point clouds is recently used in many Geomatics Engineering applications such as the building extraction in urban areas, Digital Terrain Model (DTM) generation and the road or urban furniture extraction. Segmentation is a process of dividing point clouds according to their special characteristic layers. The present paper discusses K-means and self-organizing map (SOM) which is a type of ANN (Artificial Neural Network) segmentation algorithm which treats the segmentation of point cloud. The point clouds which generate with photogrammetric method and Terrestrial Lidar System (TLS) were segmented according to surface normal, intensity and curvature. Thus, the results were evaluated. LIDAR (Light Detection and Ranging) and Photogrammetry are commonly used to obtain point clouds in many remote sensing and geodesy applications. By photogrammetric method or LIDAR method, it is possible to obtain point cloud from terrestrial or airborne systems. In this study, the measurements were made with a Leica C10 laser scanner in LIDAR method. In photogrammetric method, the point cloud was obtained from photographs taken from the ground with a 13 MP non-metric camera.