In urban areas, the shadow cast by buildings, trees along the road, abundant objects and complex image texture make the extraction of the road on very high Resolution RGB aerial image very difficult and challenging. We propose a method of road extraction from RGB aerial image in the followings steps: Shadow removal, enhanced sobel transform, keypoints extraction based on Maximally Stable Extremal Regions (MSER), feature extraction based on Speeded Up Robust Features (SURF) and road construction based on multi-resolution segmentation. The experimental results show that the proposed method achieves a good result.
In recent years, an automatic urban road extraction, as part of Intelligent Transportation research, has attracted the researchers due to the important role for the next modern transportation where urban area plays the main role within the transportation system. In this work, we propose a new combination of fuzzy ART clustering, Region growing, Morphological Operations and Radon transform (ARMOR) for automatic extraction of urban road networks from the digital surface model (DSM). The DSM data, which is based-on the elevation of surface, overcome a serious building's shadow problem as in the aerial photo image. Due to the different elevation between the road and the buildings, the thresholding technique yields a fast initial road extraction. The threshold values are obtained from Fuzzy ART clustering of the geometrical points in the histogram. The initial road is then expanded using region growing. Though most of the road regions are extracted, it contains a lot of non-road areas and the edge is still rough. A fast way to smoothing the region is by employing the morphology closing operation. Furthermore, we perform the road line filter by opening operation with a line shape structuring element, where the line orientation is obtained from the Radon Transform. Finally, the road network is constructed based-on B-Spline from the extracted road skeleton. The experimental result shows that the proposed method running faster and increases the quality and the accuracy about 10% higher than the highest result of the compared method.
We propose to use an active shape model for correcting a road map stably. Active shape model can deform itself preserving a given basic shape by restricting the deformation to an affine transformation. In order to consider a topological connections for road map, we apply the deformation to @ not single road but simultaneously several roads, one step at a time. By iterating the deformation step, road network is refined gradually. Finally, experimental results will show that proposed method can refine the existing road map correctly by fitting aerial image.
Importance for acquiring geographic map data and updating existing data is increasing. The automation of road extraction from aerial imagery has received attention. In the past, many approaches have been considered, however the existing automatic road extraction methods still need too much post editing. In this paper, we propose the method of automatic road extraction from high resolution color aerial images based on the information, such as a position and the direction of road intersection. As road shape recognition, we use an active contour model which is a kind of deformable shape model. The active contour model with a width parameter(called Ribbon Snakes)
is useful as a technique to extract the road form, however the method has a problem how to generate a initial contour. We generate a initial contour using result of a road tracking. A road tracking is performed using the information, such as a position and the direction of road intersection. To detect road intersections, we use the template matching like cross form. We report experiments using high resolution (0.5m per pixel) color aerial imagery of residential area in suburb.