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Remote Sens. 2018, 10(8), 1320; https://doi.org/10.3390/rs10081320

Window Detection from UAS-Derived Photogrammetric Point Cloud Employing Density-Based Filtering and Perceptual Organization

1
Faculty of Geodesy and Geomatics Engineering, K.N. Toosi University of Technology, 19667-15433 Tehran, Iran
2
Institute of Geodesy and Photogrammetry, Technical University of Braunschweig, 38106 Braunschweig, Germany
3
Faculty of Geomatics, Computer Science and Mathematics, University of Applied Sciences, 70174 Stuttgart, Germany
*
Author to whom correspondence should be addressed.
Received: 19 July 2018 / Revised: 7 August 2018 / Accepted: 15 August 2018 / Published: 20 August 2018
(This article belongs to the Special Issue Remote Sensing from Unmanned Aerial Vehicles (UAVs))
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Abstract

Point clouds with ever-increasing volume are regular data in 3D city modelling, in which building reconstruction is a significant part. The photogrammetric point cloud, generated from UAS (Unmanned Aerial System) imagery, is a novel type of data in building reconstruction. Its positive characteristics, alongside its challenging qualities, provoke discussions on this theme of research. In this paper, patch-wise detection of the points of window frames on facades and roofs are undertaken using this kind of data. A density-based multi-scale filter is devised in the feature space of normal vectors to globally handle the matter of high volume of data and to detect edges. Color information is employed for the downsized data to remove the inner clutter of the building. Perceptual organization directs the approach via grouping and the Gestalt principles, to segment the filtered point cloud and to later detect window patches. The evaluation of the approach displays a completeness of 95% and 92%, respectively, as well as a correctness of 95% and 96%, respectively, for the detection of rectangular and partially curved window frames in two big heterogeneous cluttered datasets. Moreover, most intrusions and protrusions cannot mislead the window detection approach. Several doors with glass parts and a number of parallel parts of the scaffolding are mistaken as windows when using the large-scale object detection approach due to their similar patterns with window frames. Sensitivity analysis of the input parameters demonstrates that the filter functionality depends on the radius of density calculation in the feature space. Furthermore, successfully employing the Gestalt principles in the detection of window frames is influenced by the width determination of window partitioning. View Full-Text
Keywords: heterogeneous image-derived point cloud; filtering; perceptual organization; window extraction; UAV (Unmanned Aerial Vehicle); edge detection; big datasets; normal vectors; clutter heterogeneous image-derived point cloud; filtering; perceptual organization; window extraction; UAV (Unmanned Aerial Vehicle); edge detection; big datasets; normal vectors; clutter
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Malihi, S.; Valadan Zoej, M.J.; Hahn, M.; Mokhtarzade, M. Window Detection from UAS-Derived Photogrammetric Point Cloud Employing Density-Based Filtering and Perceptual Organization. Remote Sens. 2018, 10, 1320.

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