POINT CLOUD CLASSIFICATION OF TESSERAE FROM TERRESTRIAL LASER DATA COMBINED WITH DENSE IMAGE MATCHING FOR ARCHAEOLOGICAL INFORMATION EXTRACTION
Florent Poux|Romain Neuville|Roland Billen
Abstract. Reasoning from information extraction given by point cloud data mining allows contextual adaptation and fast decision making. However, to achieve this perceptive level, a must be semantically rich, retaining relevant for the end user. This paper presents an automatic knowledge-based method pre-processing multi-sensory classifying hybrid both terrestrial laser scanning dense image matching. Using 18 features including sensor’s biased data, each tessera in high-density 3D captured complex mosaics of Germigny-des-prés (France) is segmented via colour multi-scale abstraction-based featuring extracting connectivity. A 2D surface outline polygon generated RANSAC plane convex hull fitting. Knowledge then used classify every tesserae based on their size, surface, shape, material properties neighbour’s class. The detection semantic enrichment shows promising results 94% correct semantization, first step toward creation archaeological smart cloud.
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences|DOAJ (DOAJ: Directory of Open Access Journals)|Open Repository and Bibliography (University of Liège)