Extraction of Urban Road Network from Multispectral Images Using Multivariate Kernel Statistics and Segmentation Method
Extraction of the urban road from multispectral images has been a challenging task in the remote sensing communities, from the last few decades. The common problems currently encountered in the extraction of urban road network are the scene covered by trees shadows and similar spectral objects, whilst, the roads has different widths and surface material. In this paper automatic road extraction algorithm is proposed. The proposed methodology is combining the ISODATA classification and the kernel statistics techniques to extract the urban road network from the remote sensing satellite images. The proposed methodology has three main steps; the first step is to perform classification of the color image, then these color classify images are converted into binary segmented images using the proposed algorithm. Secondly, the proposed algorithm is tested on the overlay color images (red line image) to detect the road network as binary images. Some filtering techniques are used to remove the redundant objects and connect the disconnected segment of the road such as segments reconstruction and region filling. Finally, postprocessing techniques are employed to extract the centerline of the urban road, such, as the thinning algorithm is used. The intended procedures are implemented on various multispectral datasets such as IKONOS and QuickBird images which contribute accurate evaluation. The methodology can extract linear features such as road network in urban environment efficiently which is useful for recognizing some of other linear features. Experimental results yield that suggested methodology is computationally robust and effective.