Extraction of Different Road Shapes from Satellite Images with Different Resolutions

The proposed semi-automatic road extraction system has been established to store and update road networks in medium and small scaled maps of the RMTO of Iran and to reduce charges and also to keep or increase precision and speed of information extraction in comparison to field work and GPS usage. Furthermore, the capability of the proposed system in extracting different shaped roads such as straight, spiral, junction and square has been investigated. The input data of the proposed road extraction system are Pansharpened and Panchromatic images of IKONOS and IRS-1D (1&4 and 5 meters spatial resolution) and Pansharpened images of ETM+ sensor of Landsat7 (15 meters spatial resolution). In the proposed method, FCM clustering technique is implemented in spectral space. Afterwards, morphological operators, as post- processing tools, are used to improve the road class image by removing noise and filling gaps and then C-Means clustering is performed on the spatial coordinates of the pixels and Key Nodes are attained. These nodes were connected using MST algorithm in graph theory by defining appropriate cost function (Euclidean distances) in order to extract road centerline which can be imported into GIS environment in vector format. Attaining mean overall accuracy (OA) of more than 98% and mean RMS error of about 0.68 pixels, when comparing automatic extracted road centerline with the manual extracted one, is a good criterion for the success of the proposed system in semi-automatic road extraction.