The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XL-7/W3
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-7/W3, 1317–1323, 2015
https://doi.org/10.5194/isprsarchives-XL-7-W3-1317-2015
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-7/W3, 1317–1323, 2015
https://doi.org/10.5194/isprsarchives-XL-7-W3-1317-2015

  30 Apr 2015

30 Apr 2015

POTENTIAL OF FULL WAVEFORM AIRBORNE LASER SCANNING DATA FOR URBAN AREA CLASSIFICATION – TRANSFER OF CLASSIFICATION APPROACHES BETWEEN MISSIONS

G. Tran1,2, D. Nguyen1,3, M. Milenkovic1, and N. Pfeifer1 G. Tran et al.
  • 1Department of Geodesy and Geoinformation, Vienna University of Technology, Austria
  • 2Department of Cartography, Hanoi University of Mining and Geology, Vietnam
  • 3Department of Photogrametry and Remote Sensing, Hanoi University of Mining and Geology, Vietnam

Keywords: LiDAR, Full-waveform, Urban classification, geophysical features, transfer

Abstract. Full-waveform (FWF) LiDAR (Light Detection and Ranging) systems have their advantage in recording the entire backscattered signal of each emitted laser pulse compared to conventional airborne discrete-return laser scanner systems. The FWF systems can provide point clouds which contain extra attributes like amplitude and echo width, etc. In this study, a FWF data collected in 2010 for Eisenstadt, a city in the eastern part of Austria was used to classify four main classes: buildings, trees, waterbody and ground by employing a decision tree. Point density, echo ratio, echo width, normalised digital surface model and point cloud roughness are the main inputs for classification. The accuracy of the final results, correctness and completeness measures, were assessed by comparison of the classified output to a knowledge-based labelling of the points. Completeness and correctness between 90% and 97% was reached, depending on the class. While such results and methods were presented before, we are investigating additionally the transferability of the classification method (features, thresholds …) to another urban FWF lidar point cloud. Our conclusions are that from the features used, only echo width requires new thresholds. A data-driven adaptation of thresholds is suggested.