Details
| Original language | English |
|---|---|
| Journal | International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives |
| Volume | 34 |
| Publication status | Published - 2002 |
| Event | 2002 International Symposium of ISPRS Commission III on Photogrammetric Computer Vision, PCV 2002 - Graz, Austria Duration: 9 Sept 2002 → 13 Sept 2002 |
Abstract
The paper presents a road junction operator, which was developed for medium resolution black-and-white orthoimages. The operator uses a feed-forward neural network applied for a running window to decide whether it contains a 3- or 4-arm road junction or not. The training set was created by a data analysis based feature selection. The best features took part in the training of 3-layer neural networks. The features are coming from the central kernel of the window (raster data) and from edge detection (vector data). The vectorized edges are only kept for training, if they are going through the central circle, which represents the junction central in a rotation invariant way. The edges fulfilling the circle criterion are applied to derive features, like edge length and direction measures. A set of identically structured networks with varied parameters was generated and trained by an efficient optimization algorithm. The evaluation of the networks was carried out in in-sample tests, where the main traditional methods are compared to the neural solution. The out-of-sample test was performed by real image chips with different rotations. The obtained results demonstrate the principal feasibility of the developed method.
Keywords
- Artificial Neural Networks, Automatic Object Extraction, Road Junctions
ASJC Scopus subject areas
- Computer Science(all)
- Information Systems
- Social Sciences(all)
- Geography, Planning and Development
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In: International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives, Vol. 34, 2002.
Research output: Contribution to journal › Conference article › Research › peer review
}
TY - JOUR
T1 - Junction extraction by artificial neural network system
T2 - 2002 International Symposium of ISPRS Commission III on Photogrammetric Computer Vision, PCV 2002
AU - Barsi, Arpad
AU - Heipke, Christian
AU - Willrich, Felicitas
N1 - Funding Information: The authors are grateful for the financial support to the Alexander von Humboldt Foundation, for the data to the Federal Agency for Cartography and Geodesy and the Hungarian Higher Education Research and Development Project.
PY - 2002
Y1 - 2002
N2 - The paper presents a road junction operator, which was developed for medium resolution black-and-white orthoimages. The operator uses a feed-forward neural network applied for a running window to decide whether it contains a 3- or 4-arm road junction or not. The training set was created by a data analysis based feature selection. The best features took part in the training of 3-layer neural networks. The features are coming from the central kernel of the window (raster data) and from edge detection (vector data). The vectorized edges are only kept for training, if they are going through the central circle, which represents the junction central in a rotation invariant way. The edges fulfilling the circle criterion are applied to derive features, like edge length and direction measures. A set of identically structured networks with varied parameters was generated and trained by an efficient optimization algorithm. The evaluation of the networks was carried out in in-sample tests, where the main traditional methods are compared to the neural solution. The out-of-sample test was performed by real image chips with different rotations. The obtained results demonstrate the principal feasibility of the developed method.
AB - The paper presents a road junction operator, which was developed for medium resolution black-and-white orthoimages. The operator uses a feed-forward neural network applied for a running window to decide whether it contains a 3- or 4-arm road junction or not. The training set was created by a data analysis based feature selection. The best features took part in the training of 3-layer neural networks. The features are coming from the central kernel of the window (raster data) and from edge detection (vector data). The vectorized edges are only kept for training, if they are going through the central circle, which represents the junction central in a rotation invariant way. The edges fulfilling the circle criterion are applied to derive features, like edge length and direction measures. A set of identically structured networks with varied parameters was generated and trained by an efficient optimization algorithm. The evaluation of the networks was carried out in in-sample tests, where the main traditional methods are compared to the neural solution. The out-of-sample test was performed by real image chips with different rotations. The obtained results demonstrate the principal feasibility of the developed method.
KW - Artificial Neural Networks
KW - Automatic Object Extraction
KW - Road Junctions
UR - http://www.scopus.com/inward/record.url?scp=85052392857&partnerID=8YFLogxK
M3 - Conference article
AN - SCOPUS:85052392857
VL - 34
JO - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives
JF - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives
SN - 1682-1750
Y2 - 9 September 2002 through 13 September 2002
ER -