Water flow based geometric active deformable model for road network
A width and color based geometric active deformable model is proposed for road network extraction from remote sensing images with minimal human interception. Orientation and width of road are computed from a single manual seed point, from which the propagation starts both right and left hand directions of the starting point, which extracts the interconnected road network from the aerial or high spatial resolution satellite image automatically. Here the propagation (like water flow in canal with defined boundary) is restricted with color and width of the road. Road extraction is done for linear, curvilinear (U shape and S shape) roads first, irrespective of width and color. Then, this algorithm is improved to extract road with junctions in a shape of L, T and X along with center line. Roads with small break or disconnected roads are also extracts by a modified version of this same algorithm. This methodology is tested and evaluated with various remote sensing images. The experimental results show that the proposed method is efficient and extracting roads accurately with less computation time. However, in complex urban areas, the identification accuracy declines due to the various sizes of obstacles, over bridges, multilane etc.
geometric active deformable model road network extraction linear feature detection semi-automatic approach water flow approach road junction recognition