Abstract
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.
Highlights
The automatic detection and reconstruction of roads has been an important topic of research in Photogrammetry and Remote Sensing for several decades
Only examples for rural areas were shown. (Ravanbakhsh et al, 2008b, Ravanbakhsh et al, 2008a) used a model based on snakes to delineate outlines of road surfaces at crossroads, including the delineation of traffic islands
The digital surface model (DSM) and the combined orthophoto are the input for extracting the features, which provide the input to the Conditional Random Fields (CRF)-based classifier
Summary
The automatic detection and reconstruction of roads has been an important topic of research in Photogrammetry and Remote Sensing for several decades. One of the main reasons for failure of road extraction algorithms in that test was the existence of crossroads, due to the fact that model assumptions about roads (e.g., the existence of parallel edges delineating a road) are hurt there. For this reason, specific models for the extraction of crossroads from images have been developed. The main reasons for failure of that method were occlusion of the road surface by cars and a complex 3D geometry, e.g. at motorway interchanges. The problem of occlusion by cars could be overcome if the position of cars were known in the images
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