Aerial images can be used to detect topographic objects, such as roads. Image data associated with the roads can be used to generate maps and navigational aids. Manual road detection in aerial images is time-consuming. Therefore, automated methods are preferred.
Some methods extracts parallel edges and extrapolate and match profiles in high-resolution images. Another method searches for an optimal path between a small number of given points. The points are then connected by dynamic programming. A model-based optimization of ribbon snake networks has also been used to improve coarsely digitized road networks in some of the applications. Another method starts with fully automatic extraction and manually edits the results. Another method complements a low-level Markov random field (MRF) model for the extraction of road ‘seeds’ and the tracking of roads with a simple clutter and occlusion model and a Kalman filter. Another method improves road detection by modeling context, such as shadows, cars or trees hindering or supporting the extraction of the road.
Learning methods use groups of parallel segments or detect ridge-like descriptors with multi-scale methods. Hough transforms can also be used.
It is desired to provide a fully automated method for detecting roads in aerial images.