Extraction of road using soft computing techniques

One of the most challenging research topics is extraction of road network automatically from remotely sensed imagery. The roads are considered as backbone and important modes of transportation. For efficient automatic road network extraction, ground points and building points extracted were integrated and it was used with multistage road network extraction model. For road network extraction, a lane detection system is considered as a more important element. From the lane marking in the roads, the roads are easily detected from the remotely sensed images. In order to detect the lanes more effectively, B-snake with fuzzy C-means (FCM) clustering is proposed in this paper. The input image is divided into equal halves by using FCM clustering algorithm. Then, the lower part is binarized by analyzing RGB channel and it is analyzed by using B-snake model because it contains more information about road lane than upper part of the image. Then, the lane detection problem is formulated by finding the set of lane model control points. A good initial position to the B-snake lane model is provided by introducing Canny/Hough estimation of vanishing points. Moreover, the external force field for lane detection is constructed by using gradient vector flow. The correspondence between B-snake and the real edge image is determined by using minimum mean square error which is presented to find out the parameters of road model iteratively. The detected lane points are fused with the road and ground points by using constraint satisfaction neural network-complementary information integration. Moreover, the road and non-road regions are separated by using region part segmentation. Finally, the medial-axis-transform-based hypothesis verification is used to automatically complete the road network extraction framework. The performance of the existing road network extraction, ground point-based road extraction model, building and ground point-based road extraction model and proposed building, ground and lane point-based road extraction model is analyzed in terms of correctness and completeness.