Abstract
Abstract. The precise classification and reconstruction of crossroads from multiple aerial images is a challenging problem in remote sensing. We apply the Markov Random Fields (MRF) 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 up to 14 different 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. One of the features is a car confidence value that is supposed 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. Whereas the method has problems in distinguishing classes having a similar appearance, it is shown to produce promising results if a reduced set of classes is considered, yielding an overall classification accuracy of 74.8%.
Highlights
The automatic detection and reconstruction of roads has been an important topic of research in Photogrammetry and Remote Sensing for several decades
In this paper we propose a new method for the classification of scenes containing crossroads as a first step of a 3D reconstruction
The images are assumed to be colour infrared (CIR) images, though the methodology can be transferred to other spectral configurations by adapting the definition of the features to be used for classification
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 noted by (Mayer et al, 2006) is 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. Extensive overviews about methods for vehicle detection from optical aerial imagery can be found in (Stilla et al, 2004) and (Hinz et al, 2006)
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
