Road Network Detection Using Probabilistic and Graph Theoretical Methods

Ünsalan, Cem; Sırmaçek, Beril

doi:10.1109/tgrs.2012.2190078

Cited by 154 publications

(80 citation statements)

References 30 publications

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“…To extract centrelines accurately, Unsalan et al [13] developed a flexible combinatorial method that relied on probabilistic and graph theory to detect and extract road networks. Boichis et al assessed an interpretation strategy system for automatically extracting road intersections from aerial images [14].…”

Section: Related Workmentioning

confidence: 99%

Extraction of Road Intersections from GPS Traces Based on the Dominant Orientations of Roads

Xing

et al. 2017

IJGI

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Many studies have used Global Navigation Satellite System (GNSS) traces to successfully extract segments of road networks because such data can be rapidly updated at a low cost. However, most studies have not focused on extracting intersections, which are indispensable parts of road networks in terms of connectivity. However, extracted intersections often present unsatisfactory precision and misleading connectivity. This study proposes a novel method for extracting road intersections from Global Position System (GPS) trace points and for capturing intersections with better accuracy. The key to improving the geometric accuracy of intersections is to identify the dominant orientations of road segments around intersections, merge similar orientations and maintain independent conflicting orientations. Extracting intersections by aligning the dominant orientations can largely reduce location offsets and road distortions. Experiments are performed to demonstrate the increased accuracy and connectivity of extracted road intersections by the proposed method.

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Section: Related Workmentioning

confidence: 99%

Extraction of Road Intersections from GPS Traces Based on the Dominant Orientations of Roads

Xing

et al. 2017

IJGI

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“…There is only one difference in class selection phase. We divide nine mean values into three ranges as follows: (4) (5) (6) Probabilities are calculated by utilizing these values and the block's region is classified afterwards.…”

Section: Naive Bayes Classifiermentioning

confidence: 99%

“…They state that proposed system gives good and accurate results on the main road segments. Ünsalan and Sirmacek [6] present a study to detect road networks. System consists of three phases which are probabilistic road centre detection, road shape extraction, and graph-theory-based road network formation.…”

Section: Introductionmentioning

confidence: 99%

Road Detection Using Classification Algorithms

Açar¹,

Bayir²

2015

JCP

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Abstract:In this study, we present a road detection method. Proposed method consists of two phases. In the first phase, a binary image is obtained by utilizing greyscale transformation and thresholding processes. In the second phase, K-Nearest Neighbours and Naive Bayes classifiers are applied on image by utilizing colour features. Road and non-road regions are determined and these two classifiers are compared with each other. Google Maps satellite images are used since they are easily accessible on the Internet. C# based software is developed for realizing and testing the methods mentioned above.

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“…By comparing the new method to the algorithm in Ref. 8, although the new algorithm only gives a single line (curve) for each road (not for road boundaries), it can produce better road connections in a general structure.…”

Section: Thin and Vague Road Identification In Aerial Imagesmentioning

confidence: 99%

Road identification in aerial images on fractional differential and one-pass ridge edge detection

Yang

Wang

2014

J. Appl. Remote Sens

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Abstract. A method for detecting and identifying thin and vague roads in aerial images is proposed. The method first smooths an image by using a Gaussian filter, then uses a fractional differential operator to enhance the image for sharpening roads, then applies a one-pass ridge edge detection algorithm to roughly detect the roads, and finally utilizes a number of post functions to accurately identify roads. For each detected point in an aerial road image, the new ridge detection algorithm detects if it is a candidate for the ridge edge points by searching through four different directions. After that, the extracted segments of lines/curves are smoothed and their gaps are linked according to preset thresholds of lengths and directions, and the noisy lines are removed based on the rules of the curve length and shape information. If roads are thick, the image can be shrunk to a road with a width less than six pixels, then the detection result for the course resolution image is mapped into the original image to accurately re-identify the road. In experiments, by comparison to traditional methods, the studied method can have better detection results for thin and vague roads, which are difficult to detect with traditional algorithms.

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Road Network Detection Using Probabilistic and Graph Theoretical Methods

Cited by 154 publications

References 30 publications

Extraction of Road Intersections from GPS Traces Based on the Dominant Orientations of Roads

Extraction of Road Intersections from GPS Traces Based on the Dominant Orientations of Roads

Road Detection Using Classification Algorithms

Road identification in aerial images on fractional differential and one-pass ridge edge detection

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