UNSUPERVISED WINDOW EXTRACTION from PHOTOGRAMMETRIC POINT CLOUDS with THERMAL ATTRIBUTES

Publikation: Beitrag in FachzeitschriftKonferenzartikelBeigetragenBegutachtung

Beitragende

  • D. Lin - , Technische Universität Dresden (Autor:in)
  • Z. Dong - , Wuhan University (Autor:in)
  • X. Zhang - , Technische Universität Dresden (Autor:in)
  • Hans Gerd Maas - , Institut für Photogrammetrie und Fernerkundung, Technische Universität Dresden (Autor:in)

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.

Details

OriginalspracheEnglisch
Seiten (von - bis)45-51
Seitenumfang7
FachzeitschriftISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Jahrgang4
Ausgabenummer2/W5
PublikationsstatusVeröffentlicht - 29 Mai 2019
Peer-Review-StatusJa

Konferenz

Titel4th ISPRS Geospatial Week 2019
Dauer10 - 14 Juni 2019
StadtEnschede
LandNiederlande

Schlagworte

Schlagwörter

  • energy optimization, feature extraction, point cloud, segmentation, thermal attribute, Window extraction