Extraction of Road Pixels from Airborne Image and Elevation Data: Focusing on Feature Selection

Abstract

Road pixel segmentation in airborne data is an important and challenging task. Recently, a sophisticated and robust approach based on superpixels and minimum cost paths has been published. In order to find out which of the numerous features are most essential, we propose a forward-search wrapper approach for feature selection which was tested with two different classifiers and with both generic and customized features. Path connecting the superpixels with a high probability of being roads are then established, filtered, and included as higher order cliques into the non-local energy minimization module thus enforcing the connectivity of the resulting road network. Two minor contributions are adjustment of a segmentation algorithm for multichannel images and an efficient application of the Dijkstra minimum path method for a sparse set of start and end nodes. The results show that both classifiers yield quite different feature sets, which speaks in favor of wrapper approaches. Also, the customized features were ranked among the most relevant, thus emphasizing the importance of sensor data fusion and higherlevel semantical concepts.