Details
| Original language | English |
|---|---|
| Pages (from-to) | 71-81 |
| Number of pages | 11 |
| Journal | Photogrammetrie, Fernerkundung, Geoinformation |
| Volume | 2013 |
| Issue number | 2 |
| Publication status | Published - 1 May 2013 |
Abstract
In this paper, we investigate full waveform lidar data acquired over the German Wadden Sea areas in the south eastern part of the North Sea. We focus especially on classification of the 3D point clouds with the aim to determine water-land-boundaries. This is a first step towards digital terrain model generation in order to analyse the terrain topography in coastal areas and, by comparing different epochs, its dynamics. For the classification of the lidar points, we learn typical class features in a training step and combine local descriptors with context information in a conditional random fields (CRF) framework, a probabilistic supervised classification approach capable of modelling contextual knowledge. We compare the results with those obtained by a fuzzy logic based approach developed specifically for the water-land- classification in Wadden Sea areas. With the latter approach we achieve a correctness rate of more than 82% for water detection. By integrating context, the results can be significantly improved by approximately 10%. Moreover, we investigate the waveform features of the data which reveals unexpected nonlinear effects concerning the decomposition of the waveforms.
Keywords
- Classification, Coast, Conditional random fields, Lidar, Water
ASJC Scopus subject areas
- Social Sciences(all)
- Geography, Planning and Development
- Physics and Astronomy(all)
- Instrumentation
- Earth and Planetary Sciences(all)
- Earth and Planetary Sciences (miscellaneous)
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In: Photogrammetrie, Fernerkundung, Geoinformation, Vol. 2013, No. 2, 01.05.2013, p. 71-81.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Water-land-classification in coastal areas with full waveform lidar data
AU - Schmidt, Alena
AU - Rottensteiner, Franz
AU - Sörgel, Uwe
PY - 2013/5/1
Y1 - 2013/5/1
N2 - In this paper, we investigate full waveform lidar data acquired over the German Wadden Sea areas in the south eastern part of the North Sea. We focus especially on classification of the 3D point clouds with the aim to determine water-land-boundaries. This is a first step towards digital terrain model generation in order to analyse the terrain topography in coastal areas and, by comparing different epochs, its dynamics. For the classification of the lidar points, we learn typical class features in a training step and combine local descriptors with context information in a conditional random fields (CRF) framework, a probabilistic supervised classification approach capable of modelling contextual knowledge. We compare the results with those obtained by a fuzzy logic based approach developed specifically for the water-land- classification in Wadden Sea areas. With the latter approach we achieve a correctness rate of more than 82% for water detection. By integrating context, the results can be significantly improved by approximately 10%. Moreover, we investigate the waveform features of the data which reveals unexpected nonlinear effects concerning the decomposition of the waveforms.
AB - In this paper, we investigate full waveform lidar data acquired over the German Wadden Sea areas in the south eastern part of the North Sea. We focus especially on classification of the 3D point clouds with the aim to determine water-land-boundaries. This is a first step towards digital terrain model generation in order to analyse the terrain topography in coastal areas and, by comparing different epochs, its dynamics. For the classification of the lidar points, we learn typical class features in a training step and combine local descriptors with context information in a conditional random fields (CRF) framework, a probabilistic supervised classification approach capable of modelling contextual knowledge. We compare the results with those obtained by a fuzzy logic based approach developed specifically for the water-land- classification in Wadden Sea areas. With the latter approach we achieve a correctness rate of more than 82% for water detection. By integrating context, the results can be significantly improved by approximately 10%. Moreover, we investigate the waveform features of the data which reveals unexpected nonlinear effects concerning the decomposition of the waveforms.
KW - Classification
KW - Coast
KW - Conditional random fields
KW - Lidar
KW - Water
UR - http://www.scopus.com/inward/record.url?scp=84877278865&partnerID=8YFLogxK
U2 - 10.1127/1432-8364/2013/0159
DO - 10.1127/1432-8364/2013/0159
M3 - Article
AN - SCOPUS:84877278865
VL - 2013
SP - 71
EP - 81
JO - Photogrammetrie, Fernerkundung, Geoinformation
JF - Photogrammetrie, Fernerkundung, Geoinformation
SN - 1432-8364
IS - 2
ER -