Details
| Original language | English |
|---|---|
| Pages (from-to) | S71-S84 |
| Journal | ISPRS Journal of Photogrammetry and Remote Sensing |
| Volume | 66 |
| Issue number | 6 SUPPL. |
| Early online date | 12 Oct 2011 |
| Publication status | Published - Dec 2011 |
Abstract
Full-waveform lidar data are increasingly being available. Morphological features can be retrieved from the echoes composing the waveforms, and are now extensively used for a large variety of land-cover mapping issues. However, the genuine contribution of these features with respect to those computed from standard discrete return lidar systems has been barely theoretically investigated. This paper therefore aims to study the potential of full-waveform data through the automatic classification of urban areas in building, ground, and vegetation points. Two waveform processing methods, namely a non-linear least squares method and a marked point process approach, are used to fit the echoes both with symmetric and asymmetric modeling functions. The performance of the extracted full-waveform features for the classification problem are then compared to a large variety of multiple-pulse features thanks to three feature selection methods. A support vector machines classifier is finally used to label the point cloud according to various scenarios based on the rank of the features. This allows to find the best classification strategy as well as the minimal feature subsets allowing to achieve the highest classification accuracy possible for each of the three feature selection methods.The results show that the echo amplitude as well as two features computed from the radiometric calibration of full-waveform data, namely the cross-section and the backscatter coefficient, significantly contribute to the high classification accuracies reported in this paper (around 95%). Conversely, features extracted from the non Gaussian modelling of the echoes are not relevant for the discrimination of vegetation, ground, and buildings in urban areas.
Keywords
- Classification, Feature selection, Full-waveform lidar data, Support vector machines, Urban areas
ASJC Scopus subject areas
- Physics and Astronomy(all)
- Atomic and Molecular Physics, and Optics
- Engineering(all)
- Engineering (miscellaneous)
- Computer Science(all)
- Computer Science Applications
- Earth and Planetary Sciences(all)
- Computers in Earth Sciences
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In: ISPRS Journal of Photogrammetry and Remote Sensing, Vol. 66, No. 6 SUPPL., 12.2011, p. S71-S84.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Relevance assessment of full-waveform lidar data for urban area classification
AU - Mallet, Clément
AU - Bretar, Frédéric
AU - Roux, Michel
AU - Soergel, Uwe
AU - Heipke, Christian
PY - 2011/12
Y1 - 2011/12
N2 - Full-waveform lidar data are increasingly being available. Morphological features can be retrieved from the echoes composing the waveforms, and are now extensively used for a large variety of land-cover mapping issues. However, the genuine contribution of these features with respect to those computed from standard discrete return lidar systems has been barely theoretically investigated. This paper therefore aims to study the potential of full-waveform data through the automatic classification of urban areas in building, ground, and vegetation points. Two waveform processing methods, namely a non-linear least squares method and a marked point process approach, are used to fit the echoes both with symmetric and asymmetric modeling functions. The performance of the extracted full-waveform features for the classification problem are then compared to a large variety of multiple-pulse features thanks to three feature selection methods. A support vector machines classifier is finally used to label the point cloud according to various scenarios based on the rank of the features. This allows to find the best classification strategy as well as the minimal feature subsets allowing to achieve the highest classification accuracy possible for each of the three feature selection methods.The results show that the echo amplitude as well as two features computed from the radiometric calibration of full-waveform data, namely the cross-section and the backscatter coefficient, significantly contribute to the high classification accuracies reported in this paper (around 95%). Conversely, features extracted from the non Gaussian modelling of the echoes are not relevant for the discrimination of vegetation, ground, and buildings in urban areas.
AB - Full-waveform lidar data are increasingly being available. Morphological features can be retrieved from the echoes composing the waveforms, and are now extensively used for a large variety of land-cover mapping issues. However, the genuine contribution of these features with respect to those computed from standard discrete return lidar systems has been barely theoretically investigated. This paper therefore aims to study the potential of full-waveform data through the automatic classification of urban areas in building, ground, and vegetation points. Two waveform processing methods, namely a non-linear least squares method and a marked point process approach, are used to fit the echoes both with symmetric and asymmetric modeling functions. The performance of the extracted full-waveform features for the classification problem are then compared to a large variety of multiple-pulse features thanks to three feature selection methods. A support vector machines classifier is finally used to label the point cloud according to various scenarios based on the rank of the features. This allows to find the best classification strategy as well as the minimal feature subsets allowing to achieve the highest classification accuracy possible for each of the three feature selection methods.The results show that the echo amplitude as well as two features computed from the radiometric calibration of full-waveform data, namely the cross-section and the backscatter coefficient, significantly contribute to the high classification accuracies reported in this paper (around 95%). Conversely, features extracted from the non Gaussian modelling of the echoes are not relevant for the discrimination of vegetation, ground, and buildings in urban areas.
KW - Classification
KW - Feature selection
KW - Full-waveform lidar data
KW - Support vector machines
KW - Urban areas
UR - http://www.scopus.com/inward/record.url?scp=84355166634&partnerID=8YFLogxK
U2 - 10.1016/j.isprsjprs.2011.09.008
DO - 10.1016/j.isprsjprs.2011.09.008
M3 - Article
AN - SCOPUS:84355166634
VL - 66
SP - S71-S84
JO - ISPRS Journal of Photogrammetry and Remote Sensing
JF - ISPRS Journal of Photogrammetry and Remote Sensing
SN - 0924-2716
IS - 6 SUPPL.
ER -