This letter proposes a semiautomatic method for 3-D road extraction in rural areas using stereoscopic aerial images. A strategy based on the dynamic programming algorithm provides a solution to the road extraction problem in the object space. The direct tracing of road centerlines in the object space necessitates mathematical relationships connecting road points in the stereoscopic image spaces and in the object space, enabling integration of radiometric information from the stereoscopic images into the associated energy function. The extraction process begins by first measuring a few seed points in one image of the stereoscopic pair and then transforming these into the object-space reference system. Experimental results show that the proposed method was efficient and usually provided accurate road centerlines.

Aerial images of urban areas have been used as base information for a diversity of applications. Considering the great quantity of tall buildings in these areas, it is important to have a method to automatically generate a product called true orthophoto mosaic, which represents all objects above the ground (buildings, bridges, etc.) in their true location. However, to create a true orthophoto, it is necessary to consider the occlusions caused by the surface height variation and to compensate for the lack of information using adjacent aerial images. The automatic occlusion detection is the bottleneck during the true orthophoto mosaic generation. The main aim of this paper is to introduce a new approach for occlusion detection - the surface-gradient-based method (SGBM) applied to a triangulated irregular network (TIN) representation. The originality of the SGBM is the occlusion detection principle, which is based on the concept of surface gradient behavior analysis over a TIN surface. The current methods interpolate a point cloud into a gridded digital surface model, which can introduce artifacts to the representation. The SGBM represents the surface as a TIN-based solid by taking into account the Delaunay constraint in the original point cloud, avoiding the interpolation step. The occlusions are then compensated using specific cost functions and refined via color blending. Experiments were performed and the results were assessed by using quality indicators (completeness), the consistency of orthoimage mosaic, and the time of processing. Experimental results demonstrated the feasibility of the SGBM for occlusion detection in the true orthophoto generation.

This paper presents an automatic methodology for road network extraction from medium-and high-resolution aerial images. It is based on two steps. In the first step, the road seeds (i.e., road...

This work proposes a three-step method for segmenting the roof planes of buildings in Airborne Laser Scanning (ALS) data. The first step aims at mainly avoiding the exhaustive search for planar roof faces throughout the ALS point cloud. Standard algorithms for processing ALS point cloud are used to isolate building regions. The second step of the proposed method consists in segmenting roof planes within building regions previously delimited. We use the RANdom SAmple Consensus (RANSAC) algorithm to detect roof plane points, taking into account two adaptive parameters for checking the consistency of ALS building points with the candidate planes: the distance between ALS building points and candidate planes; and the angle between the gradient vectors at ALS building points and the candidate planes’ normal vector. Each ALS building point is classified as consistent if computed parameters are below corresponding thresholds, which are automatically determined by thresholding histograms constructed for both parameters. As the RANSAC algorithm can generate fragmented results, in the third step, a post-processing is accomplished to merge planes that are approximately collinear and spatially close. The results show that the proposed method works properly. However, failures occur mainly in regions affected by local anomalies such as trees and antennas. Average rates around 90% and higher than 95% have been obtained for the completeness and correction quality parameters, respectively.