Details
| Original language | English |
|---|---|
| Pages (from-to) | 43-48 |
| Number of pages | 6 |
| Journal | ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| Volume | 2 |
| Issue number | 3W3 |
| Publication status | Published - 8 Oct 2013 |
| Event | Joint Workshop on Object Extraction for 3D City Models, Road Databases and Traffic Monitoring - Concepts, Algorithms and Evaluation, CMRT 2013 - Antalya, Turkey Duration: 12 Nov 2013 → 13 Nov 2013 |
Abstract
The precise classification and reconstruction of crossroads from multiple aerial images is a challenging problem in remote sensing. We apply the Conditional Random Fields (CRF) approach to this problem, a probabilistic model that can be used to consider context in classification. A simple appearance-based model is combined with a probabilistic model of the co-occurrence of class label at neighbouring image sites to distinguish classes that are relevant for scenes containing crossroads. The parameters of these models are learnt from training data. We use multiple overlap aerial images to derive a digital surface model (DSM) and a true orthophoto without moving cars. From the DSM and the orthophoto we derive feature vectors that are used in the classification. Within our framework we make use of a car detector based on support vector machines (SVM), which delivers car probability values. These values are used as additional feature to support the classification when the road surface is occluded by static cars. Our approach is evaluated on a dataset of airborne photos of an urban area by a comparison of the results to reference data. The evaluation is performed for images of different resolution. The method is shown to produce promising results when using the car probability values and higher image resolution.
Keywords
- Classification, Conditional Random Fields, Contextual, Crossroads
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. 3W3, 08.10.2013, p. 43-48.
Research output: Contribution to journal › Conference article › Research › peer review
}
TY - JOUR
T1 - The Application of a Car Confidence Feature for the Classification of Cross-Roads Using Conditional Random Fields
AU - Kosov, S.
AU - Rottensteiner, F.
AU - Heipke, C.
AU - Leitloff, J.
AU - Hinz, S.
PY - 2013/10/8
Y1 - 2013/10/8
N2 - The precise classification and reconstruction of crossroads from multiple aerial images is a challenging problem in remote sensing. We apply the Conditional Random Fields (CRF) approach to this problem, a probabilistic model that can be used to consider context in classification. A simple appearance-based model is combined with a probabilistic model of the co-occurrence of class label at neighbouring image sites to distinguish classes that are relevant for scenes containing crossroads. The parameters of these models are learnt from training data. We use multiple overlap aerial images to derive a digital surface model (DSM) and a true orthophoto without moving cars. From the DSM and the orthophoto we derive feature vectors that are used in the classification. Within our framework we make use of a car detector based on support vector machines (SVM), which delivers car probability values. These values are used as additional feature to support the classification when the road surface is occluded by static cars. Our approach is evaluated on a dataset of airborne photos of an urban area by a comparison of the results to reference data. The evaluation is performed for images of different resolution. The method is shown to produce promising results when using the car probability values and higher image resolution.
AB - The precise classification and reconstruction of crossroads from multiple aerial images is a challenging problem in remote sensing. We apply the Conditional Random Fields (CRF) approach to this problem, a probabilistic model that can be used to consider context in classification. A simple appearance-based model is combined with a probabilistic model of the co-occurrence of class label at neighbouring image sites to distinguish classes that are relevant for scenes containing crossroads. The parameters of these models are learnt from training data. We use multiple overlap aerial images to derive a digital surface model (DSM) and a true orthophoto without moving cars. From the DSM and the orthophoto we derive feature vectors that are used in the classification. Within our framework we make use of a car detector based on support vector machines (SVM), which delivers car probability values. These values are used as additional feature to support the classification when the road surface is occluded by static cars. Our approach is evaluated on a dataset of airborne photos of an urban area by a comparison of the results to reference data. The evaluation is performed for images of different resolution. The method is shown to produce promising results when using the car probability values and higher image resolution.
KW - Classification
KW - Conditional Random Fields
KW - Contextual
KW - Crossroads
UR - http://www.scopus.com/inward/record.url?scp=85048905978&partnerID=8YFLogxK
U2 - 10.5194/isprsannals-II-3-W3-43-2013
DO - 10.5194/isprsannals-II-3-W3-43-2013
M3 - Conference article
AN - SCOPUS:85048905978
VL - 2
SP - 43
EP - 48
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 - 3W3
T2 - Joint Workshop on Object Extraction for 3D City Models, Road Databases and Traffic Monitoring - Concepts, Algorithms and Evaluation, CMRT 2013
Y2 - 12 November 2013 through 13 November 2013
ER -