In recent years, the rapid increase in the demand for road information together with the availability of large volumes of high resolution Earth Observation (EO) images, have drawn remarkable interest to the use of EO images for road extraction. Among the proposed methods, the unsupervised fully-automatic ones are more efficient since they do not require human effort. Considering the proposed methods, the focus is usually to improve the road network detection, while the roads’ precise delineation has been less attended to. In this paper, we propose a new unsupervised fully-automatic road extraction method, based on the integration of the high resolution LiDAR and aerial images of a scene using Principal Component Analysis (PCA). This method discriminates the existing roads in a scene; and then precisely delineates them. Hough transform is then applied to the integrated information to extract straight lines; which are further used to segment the scene and discriminate the existing roads. The roads’ edges are then precisely localized using a projection-based technique, and the round corners are further refined. Experimental results demonstrate that our proposed method extracts and delineates the roads with a high accuracy.
CAJAL3D: Towards A Fully Automatic Pipeline for Connectome Estimation from High-Resolution EM Data