At present, remote sensing technology is the best weapon to get information from the earth surface, and it is very useful in geo- information updating and related applications. Extracting road from remote sensing images is one of the biggest demand of rapid city development, therefore, it becomes a hot issue. Roads in high-resolution images are more complex, patterns of roads vary a lot, which becomes obstacles for road extraction. In this paper, a machine learning based strategy is presented. The strategy overall uses the geometry features, radiation features, topology features and texture features. In high resolution remote sensing images, the images cover a great scale of landscape, thus, the speed of extracting roads is slow. So, roads’ ROIs are firstly detected by using Houghline detection and buffering method to narrow down the detecting area. As roads in high resolution images are normally in ribbon shape, mean-shift and watershed segmentation methods are used to extract road segments. Then, Real Adaboost supervised machine learning algorithm is used to pick out segments that contain roads’ pattern. At last, geometric shape analysis and morphology methods are used to prune and restore the whole roads’ area and to detect the centerline of roads.
MACHINE LEARNING BASED ROAD DETECTION FROM HIGH RESOLUTION IMAGERY
