Road extraction and intersection detection based on tensor voting

Zhang, Yingying; Zhang, Junping; Li, Tong; Sun, Kaiqiong

doi:10.1109/igarss.2016.7729405

Cited by 9 publications

(6 citation statements)

References 2 publications

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“…In the previous study of road extraction algorithms [10,17,29], many scholars had studied the extraction of road centerline. In order to fully verify the superiority of this algorithm, the centerline algorithm was compared with Ruyi Liu's algorithm [17], Chaudhuri's [10] algorithm, and Shi's [29] algorithm. In Chaudhuri's method, Chaudhuri segmented the enhanced image and obtained the road by the artificial template with small noise removed.…”

Section: Comparison Of the Methods For Centerline Extractionmentioning

confidence: 99%

“…There are faults for extracted centerlines, given the influence of algorithm accuracy and shadow occlusion, thus, this paper selects the tensor voting algorithm to connect the broken centerlines. The tensor voting, as a technique widely used in computer vision field, embodies outstanding performance in detecting geometrical features [17,20,29,30], which is a process of delivering each pixel's geometrical feature to its neighborhood. Thereby, the exchange of information at both ends of a fault centerline is enabled, so that the broken centerlines can be connected.…”

Section: Introductionmentioning

confidence: 99%

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Road Information Extraction from High-Resolution Remote Sensing Images Based on Road Reconstruction

Zhou

Sun

2019

Remote Sensing

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Traditional road extraction algorithms, which focus on improving the accuracy of road surfaces, cannot overcome the interference of shelter caused by vegetation, buildings, and shadows. In this paper, we extract the roads via road centerline extraction, road width extraction, broken centerline connection, and road reconstruction. We use a multiscale segmentation algorithm to segment the images, and feature extraction to get the initial road. The fast marching method (FMM) algorithm is employed to obtain the boundary distance field and the source distance field, and the branch backing-tracking method is used to acquire the initial centerline. Road width of each initial centerline is calculated by combining the boundary distance fields, before a tensor field is applied for connecting the broken centerline to gain the final centerline. The final centerline is matched with its road width when the final road is reconstructed. Three experimental results show that the proposed method improves the accuracy of the centerline and solves the problem of broken centerline, and that the method reconstructing the roads is excellent for maintain their integrity.

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Section: Comparison Of the Methods For Centerline Extractionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Road Information Extraction from High-Resolution Remote Sensing Images Based on Road Reconstruction

Zhou

Sun

2019

Remote Sensing

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“…To assess the performance of proposed network generation method, a comparison was carried out with two state-of-the-art methods: (1) morphological thinning [31] and; (2) tensor voting algorithm [32]. Figure 11 presents the comparison results on two test binary images using different methods.…”

Section: Comparison Of Methods For Network Generationmentioning

confidence: 99%

A Method for Road Network Extraction from High-Resolution SAR Imagery Using Direction Grouping and Curve Fitting

2019

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Roads are an important recognition target in synthetic aperture radar (SAR) image interpretation. Although a considerable number of high-quality SAR images are now available, the method of road extraction is lagging. To extract the road network with low missed and false rates, this paper proposed a road network extraction approach which includes line detection, road segmentation, road network extraction and optimization. First, the linear feature response and direction map are obtained from the SAR intensity image using the multiplicative Duda operation. Then, the backscattering coefficient and coefficient of variation are combined using a support vector machine to eliminate the linear structures of non-roads, and the binary image of road candidates is subsequently achieved by morphological profiles of path openings. Next, with the obtained direction map, a novel thinning method based on binary image decomposition and curve fitting is presented to obtain line segments of the road network. Finally, a series of measures which involve overlap, continuity, and junction optimization are proposed to construct the road network. In the experiments, the proposed method was applied to Radarsat-2 and TerraSAR-X high-resolution images. The experimental results showed that the proposed method had an excellent performance in terms of both completeness and correctness. one for local linear feature detection aiming to achieve road candidates, and one for global optimization processing, which generates regular road lines and connects the gaps to form a road network. With regard to local line detection, many edge detectors based on SAR image characteristics are presented, such as ratio of averages (ROA) operator [3], ratio of exponentially weighted averages (ROEWA) operator [4], multiplicative Duda operator [5], D1D2 operators [6,7], and so on. Recently, the method of deep fully convolutional neural networks was also introduced to detect road candidates [8]. As for the global optimization process, a commonly used framework is Markov random fields (MRFs) [2,6,9,10] which construct a graph model on road segments. In [11], a Bayesian framework which constructed a conditional random field (CRF) model was utilized to achieve road network optimization. Besides, the work by Lu et al. [12] proposed a method based on region-growing to extract the road network which extended road region step-by-step with automatically selected seeds. Similarly, Cheng et al. [13] presented an approach using a parallel particle filter to track road centerlines. Other methods are based on the genetic algorithm [14] and the snakes model [15] can also be found in the literature.In this article, we develop a road network extraction approach also based on the aforementioned two steps. On the subject of local line detection, the method of multiplicative Duda operators [5] is applied, which utilizes a group of sliding windows with different widths and directions to obtain linear feature responses. Compared with other line detectors, the multiplicative Duda operator ...

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“…Miao et al [22] extracted road intersection areas by tensor voting, and the roads were decomposed to isolated parts at the detected junction areas; then they were able to extract the centerlines for each individual section of the road. Zhang et al [23] used tensor voting to extract roads and road intersections from remote sensing images. Ishida et al [24] proposed two voting schemes to estimate and classify the position accurately.…”

Section: Introductionmentioning

confidence: 99%

Roads and Intersections Extraction from High-Resolution Remote Sensing Imagery Based on Tensor Voting under Big Data Environment

Sun

Zhang

2019

Wireless Communications and Mobile Computing

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Currently, big data is a new and hot object of research. In particular, the development of the Internet of things (IoT) results in a sharp increase in data. Enormous amounts of networking sensors are constantly collecting and transmitting data for storage and processing in the cloud including remote sensing data, environmental data, geographical data, etc. Road information extraction from remote sensing data is mainly researched in this paper. Roads are typical man-made objects. Extracting roads from remote sensing imagery has great significance in various applications such as GIS data updating, urban planning, navigation, and military. In this paper a multistage and multifeature method to extract roads and detect road intersections from high-resolution remotely sensed imagery based on tensor voting is presented. Firstly, the input remote sensing image is segmented into two groups including road candidate regions and nonroad regions using template matching; then we can obtain preliminary road map. Secondly, nonroad regions are removed by geometric characteristics of road (large area and long strip). Thirdly, tensor voting is used to overcome the broken roads and discontinuities caused by the different disturbing factors and then delete the nonroad areas that are mixed into the road areas due to mis-segmentation, improving the completeness of extracted roads. And then, all the road intersections are extracted by using tensor voting. The experiments are conducted on different remote sensing images to test the effectiveness of our method. The experimental results show that our method can get more complete and accurate extracted results than the state-of-the-art methods.

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Road extraction and intersection detection based on tensor voting

Cited by 9 publications

References 2 publications

Road Information Extraction from High-Resolution Remote Sensing Images Based on Road Reconstruction

Road Information Extraction from High-Resolution Remote Sensing Images Based on Road Reconstruction

A Method for Road Network Extraction from High-Resolution SAR Imagery Using Direction Grouping and Curve Fitting

Roads and Intersections Extraction from High-Resolution Remote Sensing Imagery Based on Tensor Voting under Big Data Environment

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