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The process of road extraction from high-resolution satellite images is complex, and most researchers have shown results on a few selected set of images. Based on the satellite data acquisition sensor and geolocation of the region, the type of processing varies and users tune several heuristic parameters to achieve a reasonable degree of accuracy. We exploit two salient features of roads, namely, distinct spectral contrast and locally linear trajectory, to design a multistage framework to extract roads from high-resolution multispectral satellite images. We trained four Probabilistic Support Vector Machines separately using four different categories of training samples extracted from urban/suburban areas. Dominant Singular Measure is used to detect locally linear edge segments as potential trajectories for roads. This complimentary information is integrated using an optimization framework to obtain potential targets for roads. This provides decent results in situations only when the roads have few obstacles (trees, large vehicles, and tall buildings). Linking of disjoint segments uses the local gradient functions at the adjacent pair of road endings. Region part segmentation uses curvature information to remove stray nonroad structures. Medial-Axis-Transform-based hypothesis verification eliminates connected nonroad structures to improve the accuracy in road detection. Results are evaluated with a large set of multispectral remotely sensed images and are compared against a few state-of-the-art methods to validate the superior performance of our proposed method.