Journal
ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING
Volume 67, Issue -, Pages 134-147Publisher
ELSEVIER SCIENCE BV
DOI: 10.1016/j.isprsjprs.2011.12.003
Keywords
Laser scanning; LiDAR; Calibration; Vegetation; Object based image analysis; Full-waveform
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Funding
- MA41-Stadtvermessung, City of Vienna
- Federal Ministry of Economics and Technology (BMWi), Germany [FKZ 50EE1014]
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This paper introduces a new GIS workflow for urban vegetation mapping from high-density (50 pts./m(2)) full-waveform airborne LiDAR data, combining the advantages of both raster and point cloud based analysis. Polygon segments derived by edge-based segmentation of the normalized digital surface model are used for classification. A rich set of segment features based on the point cloud and derived from full-waveform attributes is built, serving as input for a decision tree and artificial neural network (ANN) classifier. Exploratory data analysis and detailed investigation of the discriminative power of selected point cloud and full-waveform LiDAR observables indicate a high value of the occurrence of multiple distinct targets in a laser beam (i.e. 'echo ratio') for vegetation classification (98% correctness). The radiometric full-waveform observables (e.g. backscattering coefficient) do not suffice as single discriminators with low correctness values using a decision tree classifier (<= 72% correctness) but higher values with ANN (<= 95% correctness). Tests using reduced point densities indicate that the derived segment features and classification accuracies remain relatively stable even up to a reduction factor of 10 (5 pts./m(2)). In a representative study area in the City of Vienna/Austria the applicability of the developed object-based GIS workflow is demonstrated. The unique high density full-waveform LiDAR data open a new scale in 3D object characterization but demands for novel joint strategies in object-based raster and 3D point cloud analysis. (C) 2011 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS) Published by Elsevier B.V. All rights reserved.
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