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 -