Abstract
Determining samples is considered to be a precondition in deep network training and learning, but at present, samples are usually created manually, which limits the application of deep networks. Therefore, this article proposes an OpenStreetMap (OSM) data-driven method for creating road-positive samples. First, based on the OSM data, a line segment orientation histogram (LSOH) model is constructed to determine the local road direction. Secondly, a road homogeneity constraint rule and road texture feature statistical model are constructed to extract the local road line, and on the basis of the local road lines with the same direction, a polar constraint rule is proposed to determine the local road line set. Then, an iterative interpolation algorithm is used to connect the local road lines on both sides of the gaps between the road lines. Finally, a local texture self-similarity (LTSS) model is implemented to determine the road width, and the centerpoint autocorrection model and random sample consensus (RANSAC) algorithm are used to extract the road centerline; the road width and road centerline are used to complete the creation of the road-positive samples. Experiments are conducted on different scenes and different types of images to demonstrate the proposed method and compare it with other approaches. The results demonstrate that the proposed method for creating road-positive samples has great advantages in terms of accuracy and integrity.
Highlights
Extracting roads from high-resolution remote sensing images is an effective way to update road information, which can provide reference data for map updates [1] and traffic flow assignment [2] and decision-making bases for vehicle navigation [3] and smart city planning [4]
To provide reliable road-positive samples for deep learning, the following four aspects are studied in this article: considering the non-overlapping phenomenon between the OSM direction and the local road direction in the image, the local road direction is determined by using the line segment orientation histogram (LSOH) model; the local road line set is obtained by using the road homogeneity constraint rule, road texture feature statistical model, and local road line set extraction; the road line with an iterative interpolation algorithm is connected for the road line fracture; and an local texture self-similarity (LTSS) model is created to determine road width, the road centerline is obtained by using the centerpoint autocorrection model and the random sample consensus (RANSAC) algorithm, and the road-positive sample is created using the road width and road centerline
Based on prior information provided by the OSM data, we propose a method for creating road-positive samples
Summary
Extracting roads from high-resolution remote sensing images is an effective way to update road information, which can provide reference data for map updates [1] and traffic flow assignment [2] and decision-making bases for vehicle navigation [3] and smart city planning [4]. According to the time sequence of road extraction research, this article divides the road extraction methods into traditional methods and deep convolution neural network methods. Considering a road’s geometric features, the road extraction methods include parallel edge [5,6], line segment [7], and path morphology [8]. Kass et al [9] proposed a snake model, which fully uses the road geometric features and extracts roads by solving the extreme value of the energy function in a certain region. Considering the spectral texture homogeneity of a road, the classical method is an object-oriented method [10] In this method, the road is regarded as a region with a certain geometric regularity and texture homogeneity. The road region is divided by segmentation, and the road is extracted by classification and post-processing methods
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