Automatic Road Extraction from High Resolution Satellite Image using Adaptive Global Thresholding and Morphological Operations

Road network extraction from high resolution satellite images is one of the most important aspects. In the present paper, research experimentation is carried out in order to extract the roads from the high resolution satellite image using image segmentation methods. The segmentation technique is implemented using adaptive global thresholding and morphological operations. Global thresholding segments the image to fix the boundaries. To compute the appropriate threshold values several problems are also analyzed, for instance, the illumination conditions, the different type of pavement material, the presence of objects such as vegetation, vehicles, buildings etc. Image segmentation is performed using morphological approach implemented through dilation of similar boundaries and erosion of dissimilar and irrelevant boundaries decided on the basis of pixel characteristics. The roads are clearly identifiable in the final processed image, which is obtained by superimposing the segmented image over the original enhanced image. The experimental results proved that proposed approach can be used in reliable way for automatic detection of roads from high resolution satellite image. The results can be used in automated map preparation, detection of network in trajectory planning for unmanned aerial vehicles. It also has wide applications in navigation, computer vision as a predictor-corrector algorithm for estimating the road position to simulate dynamic process of road extraction. Although an expert can label road pixels from a given satellite image but this operation is prone to errors. Therefore, an automated system is required to detect the road network in a high resolution satellite image in a robust manner.