Due to the complicated background of objectives and speckle noise, it is almost impossible to extract roads directly from original synthetic aperture radar(SAR) images. A method is proposed for extraction of road netw...Due to the complicated background of objectives and speckle noise, it is almost impossible to extract roads directly from original synthetic aperture radar(SAR) images. A method is proposed for extraction of road network from high-resolution SAR image. Firstly, fuzzy C means is used to classify the filtered SAR image unsupervisedly, and the road pixels are isolated from the image to simplify the extraction of road network. Secondly, according to the features of roads and the membership of pixels to roads, a road model is constructed, which can reduce the extraction of road network to searching globally optimization continuous curves which pass some seed points. Finally, regarding the curves as individuals and coding a chromosome using integer code of variance relative to coordinates, the genetic operations are used to search global optimization roads. The experimental results show that the algorithm can effectively extract road network from high-resolution SAR images.展开更多
The construction of the road network is a very critical issue in the transportation industry.An accurate road network can effectively provide vital preconditions for logistics transportation,traffic diversion,vehicle ...The construction of the road network is a very critical issue in the transportation industry.An accurate road network can effectively provide vital preconditions for logistics transportation,traffic diversion,vehicle scheduling,etc.At present,it is still a difficult problem to achieve rapid and accurate extraction of road networks in different transportation environments.In order to solve the problem of road network automatic extraction in open-pit mines,this paper proposes a Rolling Clustering Algorithm(RCA)based on truck GPS trajectory data.The algorithm combines the advantages of road intersection recognition and trajectory clustering,which improves the accuracy of road network extraction while ensuring the topology.First,the original data are preprocessed to eliminate the influence of noise points.Next,all trajectories are divided into road segments through the identification of road intersection nodes,and rolling clustering is performed to extract road skeletons.Finally,a complete road network is generated by connecting the segments and intersection nodes.This study evaluated RCA's performance by comparing it with several representative road inference algorithms.The results show that the proposed algorithm outperformed others in terms of precision and recall.This algorithm achieves the best extraction accuracy while ensuring the road network topology.In the final validation phase,the GPS trajectory data of open-pit mine trucks are adopted for practical application.The proposed framework based on GPS trajectory provides a new solution for the road network extraction problem.展开更多
Road network extraction is vital to both vehicle navigation and road planning.Existing approaches focus on mining urban trunk roads from GPS trajectories of floating cars.However,path extraction,which plays an importa...Road network extraction is vital to both vehicle navigation and road planning.Existing approaches focus on mining urban trunk roads from GPS trajectories of floating cars.However,path extraction,which plays an important role in earthquake relief and village tour,is always ignored.Addressing this issue,we propose a novel approach of extracting campus’road network from walking GPS trajectories.It consists of data preprocessing and road centerline generation.The patrolling GPS trajectories,collected at Hunan University of Science and Technology,were used as the experimental data.The experimental evaluation results show that our approach is able to effectively and accurately extract both campus’trunk roads and paths.The coverage rate is 96.21%while the error rate is 3.26%.展开更多
文摘Due to the complicated background of objectives and speckle noise, it is almost impossible to extract roads directly from original synthetic aperture radar(SAR) images. A method is proposed for extraction of road network from high-resolution SAR image. Firstly, fuzzy C means is used to classify the filtered SAR image unsupervisedly, and the road pixels are isolated from the image to simplify the extraction of road network. Secondly, according to the features of roads and the membership of pixels to roads, a road model is constructed, which can reduce the extraction of road network to searching globally optimization continuous curves which pass some seed points. Finally, regarding the curves as individuals and coding a chromosome using integer code of variance relative to coordinates, the genetic operations are used to search global optimization roads. The experimental results show that the algorithm can effectively extract road network from high-resolution SAR images.
基金supported by the National Natural Science Foundation of China[Grant No.52374135 and No.52074205]The Shaanxi Province Metal Mine Intelligent Mining Theory and Technology Innovation Team[No.2023-CX-TD-12].
文摘The construction of the road network is a very critical issue in the transportation industry.An accurate road network can effectively provide vital preconditions for logistics transportation,traffic diversion,vehicle scheduling,etc.At present,it is still a difficult problem to achieve rapid and accurate extraction of road networks in different transportation environments.In order to solve the problem of road network automatic extraction in open-pit mines,this paper proposes a Rolling Clustering Algorithm(RCA)based on truck GPS trajectory data.The algorithm combines the advantages of road intersection recognition and trajectory clustering,which improves the accuracy of road network extraction while ensuring the topology.First,the original data are preprocessed to eliminate the influence of noise points.Next,all trajectories are divided into road segments through the identification of road intersection nodes,and rolling clustering is performed to extract road skeletons.Finally,a complete road network is generated by connecting the segments and intersection nodes.This study evaluated RCA's performance by comparing it with several representative road inference algorithms.The results show that the proposed algorithm outperformed others in terms of precision and recall.This algorithm achieves the best extraction accuracy while ensuring the road network topology.In the final validation phase,the GPS trajectory data of open-pit mine trucks are adopted for practical application.The proposed framework based on GPS trajectory provides a new solution for the road network extraction problem.
基金This project was funded by the National Natural Science Foundation of China(61872139,41871320)Provincial and Municipal Joint Fund of Hunan Provincial Natural Science Foundation of China(2018JJ4052)+2 种基金Hunan Provincial Natural Science Foundation of China(2017JJ2081)the Key Project of Hunan Provincial Education Department(17A070,19A172)the Project of Hunan Provincial Education Department(17C0646).
文摘Road network extraction is vital to both vehicle navigation and road planning.Existing approaches focus on mining urban trunk roads from GPS trajectories of floating cars.However,path extraction,which plays an important role in earthquake relief and village tour,is always ignored.Addressing this issue,we propose a novel approach of extracting campus’road network from walking GPS trajectories.It consists of data preprocessing and road centerline generation.The patrolling GPS trajectories,collected at Hunan University of Science and Technology,were used as the experimental data.The experimental evaluation results show that our approach is able to effectively and accurately extract both campus’trunk roads and paths.The coverage rate is 96.21%while the error rate is 3.26%.
