ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume IV-2/W5
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-2/W5, 45–51, 2019
https://doi.org/10.5194/isprs-annals-IV-2-W5-45-2019
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-2/W5, 45–51, 2019
https://doi.org/10.5194/isprs-annals-IV-2-W5-45-2019

  29 May 2019

29 May 2019

UNSUPERVISED WINDOW EXTRACTION FROM PHOTOGRAMMETRIC POINT CLOUDS WITH THERMAL ATTRIBUTES

D. Lin1, Z. Dong2, X. Zhang1, and H.-G. Maas1 D. Lin et al.
  • 1Technische Universität Dresden, Institute of Photogrammetry and Remote Sensing, Dresden, Germany
  • 2State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, China

Keywords: window extraction, point cloud, thermal attribute, segmentation, energy optimization, feature extraction

Abstract. 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.