Abstract
Abstract. Extracting roads from aerial images is a challenging task in the field of remote sensing. Most approaches formulate road extraction as a segmentation problem and use thinning and edge detection to obtain road centerlines and edge lines, which could produce spurs around the extracted centerlines/edge lines. In this study, a novel regression-based method is proposed to extract road centerlines and edge lines directly from aerial images. The method consists of three major steps. First, an end-to-end regression network based on CNN is trained to predict confidence maps for road centerlines and estimate road width. Then, after the CNN predicts the confidence map, non-maximum suppression and road tracking are applied to extract accurate road centerlines and construct road topology. Meanwhile, Road edge lines are generated based on the road width estimated by the CNN. Finally, in order to improve the connectivity of extracted road network, tensor voting is applied to detect road intersections and the detected intersections are used as guidance for the overcome of discontinuities. The experiments conducted on the SpaceNet and DeepGlobe datasets show that our approach achieves better performance than other methods.
Highlights
Road extraction from high-resolution remote sensing images is an essential task in the field of remote sensing
In order to improve the connectivity of extracted road network, tensor voting is applied to detect road intersections and we use detected intersections as the guidance for the overcome of the discontinuities
This study proposes a regression-based method for automatic extraction of road centerlines and edge lines from aerial images
Summary
Road extraction from high-resolution remote sensing images is an essential task in the field of remote sensing. It has a wide range of applications, such as vehicle navigation, urban planning, autonomous driving and automatic digital line graphic making. To deal with road extraction task, many CNN-based methods (Panboonyuen T et al, 2017) have been proposed. Most of these approaches formulated road extraction as a segmentation problem. (2) The road topology is not taken into account It is of great practical significance with a framework to directly extract road centrelines and edge lines from satellite images. The belief maps encode the spatial uncertainty of each keypoint’s location
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