Semi-automatic Extraction of Different-shaped Road Centerlines from Ms and Pan-sharped Ikonos Images

In this work we develop semi-automatic road extraction system for updating and storage road network data bases. Combination of some of the existing road extraction techniques such as spectral and spatial data clustering, morphological functions and graph theory is used in this proposed system. Input data of the proposed road extraction system are multi-spectral and pan-sharpened IKONOS images of Lavasan city in Iran (with respectively 4 and 1 meters spatial resolution). The proposed system investigates capability and amount of system success in extraction of different shaped roads such as straight, spiral, junction and square. In the proposed method, primarily the input image is spectrally classified by use of Fuzzy C-Means (FCM) clustering technique and road class binary image is obtained by definition of threshold value. Afterwards, quality of detected road features is improved using morphological operators like dilation, erosion, opening, closing, bridge and etc. Our approach proceeds by performing spatial cluster analysis using C-Means technique and hence, road centerline nodes are attained. Finally by use of graph theory and minimum spanning tree (MST) and defining an appropriate cost function, these key points are connected and vector road centerline is obtained. The only drawback of this system is limitation in completely extraction of road centerline in place of squares and closed loops. Attaining mean overall accuracy (OA) of 98.2% and Kappa coefficient of 86.26% in classification of image to road and non-road classes, and also mean RMS error of 0.64 pixel in comparing automatic extracted road centerline with manual extracted one, are a good criterion of proposed system success in semi-automatic extraction of roads.