ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-2/W2, 203-211, 2017
https://doi.org/10.5194/isprs-annals-IV-2-W2-203-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 4.0 License.
 
16 Aug 2017
POINT CLOUD CLASSIFICATION OF TESSERAE FROM TERRESTRIAL LASER DATA COMBINED WITH DENSE IMAGE MATCHING FOR ARCHAEOLOGICAL INFORMATION EXTRACTION
F. Poux, R. Neuville, and R. Billen Ulg, Geomatics Unit, University of Liège, 4000 Liège, Belgium
Keywords: point cloud, data fusion, feature extraction, segmentation, classification, cultural heritage, laser scanning 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 point cloud must be semantically rich, retaining relevant information for the end user. This paper presents an automatic knowledge-based method for pre-processing multi-sensory data and classifying a hybrid point cloud from both terrestrial laser scanning and dense image matching. Using 18 features including sensor’s biased data, each tessera in the high-density point cloud from the 3D captured complex mosaics of Germigny-des-prés (France) is segmented via a colour multi-scale abstraction-based featuring extracting connectivity. A 2D surface and outline polygon of each tessera is generated by a RANSAC plane extraction and convex hull fitting. Knowledge is then used to classify every tesserae based on their size, surface, shape, material properties and their neighbour’s class. The detection and semantic enrichment method shows promising results of 94% correct semantization, a first step toward the creation of an archaeological smart point cloud.
Conference paper (PDF, 2643 KB)


Citation: Poux, F., Neuville, R., and Billen, R.: POINT CLOUD CLASSIFICATION OF TESSERAE FROM TERRESTRIAL LASER DATA COMBINED WITH DENSE IMAGE MATCHING FOR ARCHAEOLOGICAL INFORMATION EXTRACTION, ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-2/W2, 203-211, https://doi.org/10.5194/isprs-annals-IV-2-W2-203-2017, 2017.

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