Urban road classification in geometrically integrated high-resolution RGB aerial and laser-derived images using the artificial neural network classification method

Abstract

The problem of automated urban road network extraction is extremely complex because roads in urban scenes strongly interact with other objects. This problem can be simplified if road regions are first isolated using a classification procedure. The isolated road regions can be posteriorly used in tasks of refinement and reconstruction of the road network. This article addresses only the problem of road regions’ detection using Artificial Neural Network as classification method. However, in urban areas, the use of spectral data alone commonly leads to the confusion of the road class with other classes in RGB images, such as building roofs and concrete, because these objects may present similar spectral characteristics. To overcome this problem, it is proposed the integration of a high-resolution RGB aerial image with laser-derived images. The classification results showed that the integration of the geometric (height) and radiometric (laser pulse intensity) laser data significantly improved the classification accuracy, also contributing for the better detection of road pixel. The laser intensity data help to overcome the effects of road obstructions caused by shadows and trees. On the other hand, the laser height data help to separate the aboveground objects from those on the ground level.