Road detection from remotely sensed images using color features

Urban regions are dynamic environments. Especially their road maps change by the expansion of the urban region. Therefore, automatic detection of roads from very high resolution aerial and satellite images is a very important research field. Unfortunately, the solution is not straightforward by using basic image processing and computer vision algorithms. Therefore, advanced methods are needed for road network detection from aerial and satellite images. In this study, we propose a novel method for automatic detection of road segments from very high resolution color aerial and satellite images. Our method depends on choosing a training set from the input image manually. We use color chroma values of pixels as the discriminative features. Since road pixels have similar color characteristics, the distribution of color chroma feature values of the training region have a peak at a certain point in the feature space which shows the road class. Using this information and one-class classification methodology, we label road segments in a given remotely sensed image. Finally, we fit a road network shape on the detected segment. Experimental results on color aerial and Ikonos satellite images show the importance of color features in road detection applications.