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Open AccessArticle

Supervised Detection of Façade Openings in 3D Point Clouds with Thermal Attributes

1
Institute of Photogrammetry and Remote Sensing, Technische Universität Dresden, 01069 Dresden, Germany
2
Institute of Geodesy and Geoinformatics, Wroclaw University of Environmental and Life Sciences, 50-375 Wroclaw, Poland
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(3), 543; https://doi.org/10.3390/rs12030543
Received: 13 December 2019 / Revised: 29 January 2020 / Accepted: 2 February 2020 / Published: 6 February 2020
Targeted energy management and control is becoming an increasing concern in the building sector. Automatic analyses of thermal data, which minimize the subjectivity of the assessment and allow for large-scale inspections, are therefore of high interest. In this study, we propose an approach for a supervised extraction of façade openings (windows and doors) from photogrammetric 3D point clouds attributed to RGB and thermal infrared (TIR) information. The novelty of the proposed approach is in the combination of thermal information with other available characteristics of data for a classification performed directly in 3D space. Images acquired in visible and thermal infrared spectra serve as input data for the camera pose estimation and the reconstruction of 3D scene geometry. To investigate the relevance of different information types to the classification performance, a Random Forest algorithm is applied to various sets of computed features. The best feature combination is then used as an input for a Conditional Random Field that enables us to incorporate contextual information and consider the interaction between the points. The evaluation executed on a per-point level shows that the fusion of all available information types together with context consideration allows us to extract objects with 90% completeness and 95% correctness. A respective assessment executed on a per-object level shows 97% completeness and 88% accuracy. View Full-Text
Keywords: thermal infrared imagery; 3D point cloud; semantic classification; building façades thermal infrared imagery; 3D point cloud; semantic classification; building façades
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MDPI and ACS Style

Jarząbek-Rychard, M.; Lin, D.; Maas, H.-G. Supervised Detection of Façade Openings in 3D Point Clouds with Thermal Attributes. Remote Sens. 2020, 12, 543. https://doi.org/10.3390/rs12030543

AMA Style

Jarząbek-Rychard M, Lin D, Maas H-G. Supervised Detection of Façade Openings in 3D Point Clouds with Thermal Attributes. Remote Sensing. 2020; 12(3):543. https://doi.org/10.3390/rs12030543

Chicago/Turabian Style

Jarząbek-Rychard, Małgorzata; Lin, Dong; Maas, Hans-Gerd. 2020. "Supervised Detection of Façade Openings in 3D Point Clouds with Thermal Attributes" Remote Sens. 12, no. 3: 543. https://doi.org/10.3390/rs12030543

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