Kosov, S. G.; Rottensteiner, F.; Heipke, C.; Leitloff, J.; Hinz, S.: The application of a car confidence feature for the classification of cross-roads using conditional random fields. In: ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences II-3 (2013), Nr. W3 , S. 43-48. DOI: https://doi.org/10.5194/isprsannals-II-3-W3-43-2013
Zusammenfassung: | |
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. | |
Lizenzbestimmungen: | CC BY 3.0 Unported |
Publikationstyp: | Article |
Publikationsstatus: | publishedVersion |
Erstveröffentlichung: | 2013 |
Die Publikation erscheint in Sammlung(en): | Fakultät für Bauingenieurwesen und Geodäsie |
Pos. | Land | Downloads | ||
---|---|---|---|---|
Anzahl | Proz. | |||
1 | Germany | 100 | 67,11% | |
2 | United States | 23 | 15,44% | |
3 | China | 9 | 6,04% | |
4 | Indonesia | 3 | 2,01% | |
5 | Nepal | 2 | 1,34% | |
6 | Russian Federation | 1 | 0,67% | |
7 | Romania | 1 | 0,67% | |
8 | Philippines | 1 | 0,67% | |
9 | Korea, Republic of | 1 | 0,67% | |
10 | Israel | 1 | 0,67% | |
andere | 7 | 4,70% |
Hinweis
Zur Erhebung der Downloadstatistiken kommen entsprechend dem „COUNTER Code of Practice for e-Resources“ international anerkannte Regeln und Normen zur Anwendung. COUNTER ist eine internationale Non-Profit-Organisation, in der Bibliotheksverbände, Datenbankanbieter und Verlage gemeinsam an Standards zur Erhebung, Speicherung und Verarbeitung von Nutzungsdaten elektronischer Ressourcen arbeiten, welche so Objektivität und Vergleichbarkeit gewährleisten sollen. Es werden hierbei ausschließlich Zugriffe auf die entsprechenden Volltexte ausgewertet, keine Aufrufe der Website an sich.