Roads extraction from VHR satellite images are very paramount for GIS and map updating. Due to the high resolution of satellite images, there are many obstacles broken roads such as shadow, and vehicles. The present work aims to find the most suitable road extraction approach that can be applied in the Egyptian environment. In this study, two satellite images from WorldView-2 and WorldView-3 were used. Classification of image by pixel-based and object-based was carried out to find the appropriate classification method for road extraction. Then, road class refinement by morphology and angular texture signature are performed to decrease the misclassifications between roads and other spectrally similar objects. After that, an iterative and localized Hough transform method was compared with the thinning algorithm method to find the proper method that can extract road centerline segments from the refined images. The performance of the extracted roads was estimated by using the common metrics; completeness, correctness, and quality. The results of this work demonstrate that the random tree in object-based classification achieves the highest overall accuracy than other classification methods. Also, thinning algorithm has more advantages than Hough transform.