Details
| Original language | English |
|---|---|
| Pages (from-to) | 271-278 |
| Number of pages | 8 |
| Journal | ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| Volume | 2 |
| Issue number | 3W4 |
| Publication status | Published - 12 Mar 2015 |
| Event | Joint ISPRS workshops on Photogrammetric Image Analysis, PIA 2015 and High Resolution Earth Imaging for Geospatial Information, HRIGI 2015 - Munich, Germany Duration: 25 Mar 2015 → 27 Mar 2015 |
Abstract
The fully automated analysis of 3D point clouds is of great importance in photogrammetry, remote sensing and computer vision. For reliably extracting objects such as buildings, road inventory or vegetation, many approaches rely on the results of a point cloud classification, where each 3D point is assigned a respective semantic class label. Such an assignment, in turn, typically involves statistical methods for feature extraction and machine learning. Whereas the different components in the processing workflow have extensively, but separately been investigated in recent years, the respective connection by sharing the results of crucial tasks across all components has not yet been addressed. This connection not only encapsulates the interrelated issues of neighborhood selection and feature extraction, but also the issue of how to involve spatial context in the classification step. In this paper, we present a novel and generic approach for 3D scene analysis which relies on (i) individually optimized 3D neighborhoods for (ii) the extraction of distinctive geometric features and (iii) the contextual classification of point cloud data. For a labeled benchmark dataset, we demonstrate the beneficial impact of involving contextual information in the classification process and that using individual 3D neighborhoods of optimal size significantly increases the quality of the results for both pointwise and contextual classification.
Keywords
- 3D Scene Analysis, Classification, Contextual Learning, Features, Laser Scanning, Lidar, Point Cloud, Urban
ASJC Scopus subject areas
- Earth and Planetary Sciences(all)
- Earth and Planetary Sciences (miscellaneous)
- Environmental Science(all)
- Environmental Science (miscellaneous)
- Physics and Astronomy(all)
- Instrumentation
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In: ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol. 2, No. 3W4, 12.03.2015, p. 271-278.
Research output: Contribution to journal › Conference article › Research › peer review
}
TY - JOUR
T1 - Contextual classification of point cloud data by exploiting individual 3D neigbourhoods
AU - Weinmann, M.
AU - Schmidt, A.
AU - Mallet, C.
AU - Hinz, S.
AU - Rottensteiner, F.
AU - Jutzi, B.
PY - 2015/3/12
Y1 - 2015/3/12
N2 - The fully automated analysis of 3D point clouds is of great importance in photogrammetry, remote sensing and computer vision. For reliably extracting objects such as buildings, road inventory or vegetation, many approaches rely on the results of a point cloud classification, where each 3D point is assigned a respective semantic class label. Such an assignment, in turn, typically involves statistical methods for feature extraction and machine learning. Whereas the different components in the processing workflow have extensively, but separately been investigated in recent years, the respective connection by sharing the results of crucial tasks across all components has not yet been addressed. This connection not only encapsulates the interrelated issues of neighborhood selection and feature extraction, but also the issue of how to involve spatial context in the classification step. In this paper, we present a novel and generic approach for 3D scene analysis which relies on (i) individually optimized 3D neighborhoods for (ii) the extraction of distinctive geometric features and (iii) the contextual classification of point cloud data. For a labeled benchmark dataset, we demonstrate the beneficial impact of involving contextual information in the classification process and that using individual 3D neighborhoods of optimal size significantly increases the quality of the results for both pointwise and contextual classification.
AB - The fully automated analysis of 3D point clouds is of great importance in photogrammetry, remote sensing and computer vision. For reliably extracting objects such as buildings, road inventory or vegetation, many approaches rely on the results of a point cloud classification, where each 3D point is assigned a respective semantic class label. Such an assignment, in turn, typically involves statistical methods for feature extraction and machine learning. Whereas the different components in the processing workflow have extensively, but separately been investigated in recent years, the respective connection by sharing the results of crucial tasks across all components has not yet been addressed. This connection not only encapsulates the interrelated issues of neighborhood selection and feature extraction, but also the issue of how to involve spatial context in the classification step. In this paper, we present a novel and generic approach for 3D scene analysis which relies on (i) individually optimized 3D neighborhoods for (ii) the extraction of distinctive geometric features and (iii) the contextual classification of point cloud data. For a labeled benchmark dataset, we demonstrate the beneficial impact of involving contextual information in the classification process and that using individual 3D neighborhoods of optimal size significantly increases the quality of the results for both pointwise and contextual classification.
KW - 3D Scene Analysis
KW - Classification
KW - Contextual Learning
KW - Features
KW - Laser Scanning
KW - Lidar
KW - Point Cloud
KW - Urban
UR - http://www.scopus.com/inward/record.url?scp=84969193116&partnerID=8YFLogxK
U2 - 10.5194/isprsannals-II-3-W4-271-2015
DO - 10.5194/isprsannals-II-3-W4-271-2015
M3 - Conference article
AN - SCOPUS:84969193116
VL - 2
SP - 271
EP - 278
JO - ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
JF - ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
SN - 2194-9042
IS - 3W4
T2 - Joint ISPRS workshops on Photogrammetric Image Analysis, PIA 2015 and High Resolution Earth Imaging for Geospatial Information, HRIGI 2015
Y2 - 25 March 2015 through 27 March 2015
ER -