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Weakly Supervised Network with Scribble-Supervised and Edge-Mask for road extraction from High-Resolution Remote Sensing Images

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Computers, Materials & Continua 2024年第4期79卷 549-562页

作者： Supeng Yu Fen Huang Chengcheng Fan College of Artificial Intelligence Nanjing Agricultural UniversityNanjing210095China Innovation Academy for Microsatellites of CAS Shanghai201210China Shanghai Engineering Center for Microsatellites Shanghai201210China Key Laboratory of Satellite Digital Technology Shanghai201210China

Significant advancements have been achieved in road surface extraction based on high-resolution remote sensingimage processing. Most current methods rely on fully supervised learning, which necessitates enormous humaneffort to label the image. Within this field, other research endeavors utilize weakly supervised methods. Theseapproaches aim to reduce the expenses associated with annotation by leveraging sparsely annotated data, such asscribbles. This paper presents a novel technique called a weakly supervised network using scribble-supervised andedge-mask (WSSE-net). This network is a three-branch network architecture, whereby each branch is equippedwith a distinct decoder module dedicated to road extraction tasks. One of the branches is dedicated to generatingedge masks using edge detection algorithms and optimizing road edge details. The other two branches supervise themodel’s training by employing scribble labels and spreading scribble information throughout the image. To addressthe historical flaw that created pseudo-labels that are not updated with network training, we use mixup to blendprediction results dynamically and continually update new pseudo-labels to steer network training. Our solutiondemonstrates efficient operation by simultaneously considering both edge-mask aid and dynamic pseudo-labelsupport. The studies are conducted on three separate road datasets, which consist primarily of high-resolutionremote-sensing satellite photos and drone images. The experimental findings suggest that our methodologyperforms better than advanced scribble-supervised approaches and specific traditional fully supervised methods.

关键词： Semantic segmentation road extraction weakly supervised learning scribble supervision remote sensing image

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Multiple Saliency Features Based Automatic road extraction from High-Resolution Multispectral Satellite Images

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Chinese Journal of Electronics 2018年第1期27卷 133-139页

作者： ZHANG Jing CHEN Lu ZHUO Li GENG Wenhao WANG Chao Signal and Information Processing Laboratory Beijing University of Technology

roads as important artificial objects are the main body of modern traffic system, which provide many conveniences for human civilization. With the development of remote sensing and hyperspectral imaging technology, how to automatically and accurately extract road network from high-resolution multispectral satellite images has become a hot and challenging research topic of geographic information technology. In this paper, an automatic road extraction method from high-resolution multispectral satellite images is proposed by using multiple saliency features. Firstly, road edge is extracted by detecting local linear edge with Singular value decomposition(SVD). Secondly, road regions are constructed by K-means clustering after extracting the feature of background difference. Then road network is achieved by integrating multiple saliency features with Total variation(TV) based image fusion algorithm. Finally, the non-road parts and noises are removed from road network by optimizing multiple salient features with post-processing and morphological operations. The experimental results show that the proposed method can achieve a superior performance in completeness and correctness.

关键词： High-resolution multispectral satellite images road extraction Multiple saliency features road network

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A review of road extraction from remote sensing images

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Journal of Traffic and Transportation Engineering(English Edition) 2016年第3期3卷 271-282页

作者： Weixing Wang Nan Yang Yi Zhang Fengping Wang Ting Cao Patrik Eklund School of Information Engineering Chang'an UniversityXi'an 710064China Royal Institute of Technology StockholmSweden Department of Computer Science and Technology Umeu UniversityUmeuSweden

As a significant role for traffic management, city planning, road monitoring, GPS navigation and map updating, the technology of road extraction from a remote sensing （RS） image has been a hot research topic in recent years. In this paper, after analyzing different road features and road models, the road extraction methods were classified into the classification-based methods, knowledge-based methods, mathematical morphology, active contour model, and dynamic programming. Firstly, the road features, road model, existing difficulties and interference factors for road extraction were analyzed. Secondly, the principle of road extraction, the advantages and disadvantages of various methods and research achievements were briefly highlighted. Then, the comparisons of the different road extraction algorithms were performed, including road features, test samples and shortcomings. Finally, the research results in recent years were summarized emphatically. It is obvious that only using one kind of road features is hard to get an excellent extraction effect. Hence, in order to get good results, the road extraction should combine multiple methods according to the real applications. In the future, how to realize the complete road extraction from a RS image is still an essential but challenging and important research topic.

关键词： Remote sensing image road extraction road feature Classification

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A road extraction Method for Remote Sensing Image Based on Encoder-Decoder Network

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Journal of Geodesy and Geoinformation Science 2020年第2期3卷 16-25页

作者： Hao HE Shuyang WANG Shicheng WANG Dongfang YANG Xing LIU The Rocker Force University of Engineering The Department of Control EngineeringXi’an 710025China The Rocker Force University of Engineering The Department of Information EngineeringXi’an 710025China

According to the characteristics of the road features,an Encoder-Decoder deep semantic segmentation network is designed for the road extraction of remote sensing images.Firstly,as the features of the road target are rich in local details and simple in semantic features,an Encoder-Decoder network with shallow layers and high resolution is designed to improve the ability to represent detail information.Secondly,as the road area is a small proportion in remote sensing images,the cross-entropy loss function is improved,which solves the imbalance between positive and negative samples in the training process.Experiments on large road extraction datasets show that the proposed method gets the recall rate 83.9%,precision 82.5%and F1-score 82.9%,which can extract the road targets in remote sensing images completely and accurately.The Encoder-Decoder network designed in this paper performs well in the road extraction task and needs less artificial participation,so it has a good application prospect.

关键词： remote sensing road extraction deep learning semantic segmentation Encoder-Decoder network

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road network extraction from high resolution satellite images

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Computer Aided Drafting,Design and Manufacturing 2016年第2期26卷 1-7页

作者： Li Gang Lai Shunnan Li Sheng Peking University Shenzhen Graduate School Shenzhen 518055 China School of Electronics Engineering and Computer Science Peking University Beijing 100871 China Beijing Engineering Technology Research Center of Virtual Simulation and Visualization Beijing 100871 China

In this paper, an approach of roads network extraction from high resolution satellite images is presented. First, the approach extracts road surface from satellite image using one-class support vector machine （SVM）. Second, the road topology is built from the road surface. The last output of the approach is a series of road segments which is represented by a sequence of points as well as the topological relations among them. The approach includes four steps. In the first step one-class support vector machine is used for classifying pixel of the satellite images to road class or non-road class. In the second step filling holes and connecting gaps for the SVM＇s classification result is applied through mathematical morphology close operation. In the third step the road segment is extracted by a series of operations which include skeletonization, thin, branch pruning and road segmentation. In the last step a geometrical adjustment process is applied through analyzing the road segment curvature. The experiment results demonstrate its robustness and viability on extracting road network from high resolution satellite images.

关键词： road extraction topology mathematical morphology skeletonization support vector machine

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extraction of straight field roads between farmlands based on agricultural vehicle-mounted LiDAR

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International Journal of Agricultural and Biological Engineering 2022年第5期15卷 155-162页

作者： Lili Yang Yuanyuan Xu Yajie Liang Jia Qin Yuanbo Li Xinxin Wang Weixin Zhai Long Wen Zhibo Chen Caicong Wu College of Information and Electrical Engineering China Agricultural UniversityBeijing 100083China Key Laboratory of Agricultural Machinery Monitoring and Big Data Applications Ministry of Agriculture and Rural AffairsBeijing 100083China

The application of autonomous agricultural vehicles is gaining popularity as a way to increase production efficiency and lower operational costs.To achieve high performance,perception tasks(such as obstacle detection,road extraction,and drivable area extraction)are of great importance.Compared with structured roads,field roads between farmlands,including unstructured roads and semi-structured roads,are unfavorable for autonomous agricultural vehicle driving due to their bumpiness and unstructured nature.This study proposed an extraction method for the straight field roads between farmlands.The proposed method was based on the point cloud data acquired by LiDAR(Velodyne VLP-16)mounted on a John Deere 12046B-1204 tractor.The proposed method has three aspects:Euclidean Clustering-based extraction,boundary-based extraction,and road point cloud curve segment modification.Firstly,Euclidean Clustering with K-Dimensional(KD)-Tree data structure was adopted to extract the road curve segments close to the LiDAR composed of road points.Secondly,the boundary lines constraint was constructed to extract the distant road curve segments.Thirdly,the local distance ratio was used to modify the extracted road curve segments.The average extraction accuracy for both semi-structured and unstructured roads exceeded 98%,and the false positive rate(FPR)was less than 0.5%.These experimental findings demonstrated that the proposed road extraction method was precise and effective.The proposed method of this study can be applied to enhance the perception ability of autonomous agricultural vehicles thereby increasing the efficiency and safety of field road driving.

关键词： road extraction straight field road autonomous agricultural vehicle LiDAR farmland

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road extraction based on KFCM clustering and Morphological operations

Road extraction based on KFCM clustering and Morphological o...

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第30届中国控制与决策会议

作者： Rui Luo Xiaodong Liu Yan Ren Tianci Ao Jie Xi School of electronic information and electrical engineering Dalian University of Technology Shenyang Aerospace University

road extraction from complex backgrounds plays a significant role in a wide range of applications.In this paper,a method based on the clustering and morphological operations for road extraction is proposed.First,a novel feature image containing three features of the original image is built for clustering instead of using the original image directly;then,kernel fuzzy c-means（KFCM） clustering is used to extract road area roughly;finally,morphological operations are integrated to eliminate non road areas and improve road smoothness.The experiment results show that,compared with the original image,the feature image can not only reduce the running time of the clustering process,but also improve the anti-interference ability of clustering and robustness in the subsequent morphological processing.

关键词： road extraction Feature image Kernel fuzzy c-means clustering Morphological operations

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