NDLI: UNSUPERVISED WINDOW EXTRACTION FROM PHOTOGRAMMETRIC POINT CLOUDS WITH THERMAL ATTRIBUTES

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UNSUPERVISED WINDOW EXTRACTION FROM PHOTOGRAMMETRIC POINT CLOUDS WITH THERMAL ATTRIBUTES

Content Provider	Scilit
Author	Lin, D. Dong, Z. Zhang, X. Maas, H.-G.
Copyright Year	2019
Description	The automatic extraction of windows from photogrammetric data has achieved increasing attention in recent times. An unsupervised windows extraction approach from photogrammetric point clouds with thermal attributes is proposed in this study. First, point cloud segmentation is conducted by a popular workflow: Multiscale supervoxel generation is applied to the image-based 3D point cloud, followed by region growing and energy optimization using spatial positions and thermal attributes of the raw points. Afterwards, an object-based feature (window index) is extracted using the average thermal attribute and the size of the object. Next, thresholding is applied to extract initial window regions. Finally, several criterions are applied to further refine the extraction results. For practical validation, the approach is evaluated on an art nouveau building row façade located at Dresden, Germany.
Ending Page	51
Page Count	7
Starting Page	45
e-ISSN	21949050
DOI	10.5194/isprs-annals-iv-2-w5-45-2019
Journal	ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Volume Number	IV-2/W5
Language	English
Publisher	Copernicus GmbH
Publisher Date	2019-05-29
Access Restriction	Open
Subject Keyword	Cybernetical Science Point Cloud Thermal Attributes Windows Extraction
Content Type	Text
Resource Type	Article
Subject	Earth and Planetary Sciences Environmental Science Instrumentation

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