Road Detection from Optical Remote Sensing Imagery Using Circular Projection Matching and Tracking Strategy

Fu, Gang; Zhao, Huijuan; Li, Cong; Shi, Larry

doi:10.1007/s12524-013-0295-y

Cited by 19 publications

(14 citation statements)

References 19 publications

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“…In template matching methods, a specific matching window (e.g. rectangular window [46][47]， circular window [48] or T-shaped window [49], etc.) is constructed at the starting point.…”

Section: Methods Based On Template Matchingmentioning

confidence: 99%

Road Extraction Methods in High-Resolution Remote Sensing Images: A Comprehensive Review

Lian

Wang

Mustafa

et al. 2020

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

103

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Road extraction from high-resolution remote sensing images (HRSI) is a challenging but hot research topic in the past decades. A large number of methods are invented to deal with this problem. This article provides a comprehensive review of these existing approaches. We classified the methods into heuristic and data-driven. The heuristic methods are the mainstream in the early years, and the data-driven methods based on deep learning have been quickly developed recently. With regard to the heuristic methods, the road feature model is firstly introduced, then, the classic extraction methods are reviewed in two sub-categories: semi-automatic and automatic. The principles, inspirations, advantages and disadvantages of these methods are described. In terms of the data-driven methods, the road extraction methods based on deep neural network, particularly those based on patched convolutional neural network, fully convolutional network and generative adversarial network are reviewed. We perform subjective comparisons between the methods inner each type. Furthermore, the quantity performances achieved on the same dataset are compared between the heuristic and data-driven methods to show the strengthening of the data-driven methods. Finally, the conclusion and prospects are summarized.

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“…In template matching methods, a specific matching window (e.g. rectangular window [46][47]， circular window [48] or T-shaped window [49], etc.) is constructed at the starting point.…”

Section: Methods Based On Template Matchingmentioning

confidence: 99%

Road Extraction Methods in High-Resolution Remote Sensing Images: A Comprehensive Review

Lian

Wang

Mustafa

et al. 2020

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

103

View full text Add to dashboard Cite

show abstract

“…The local image information matches the formulated rules, and the objects are recognized. Accordingly, templates can generally be divided into regular and variable templates, among them 1) regular template: Classical templates include the profile template [24], [25], the rectangular template [26], the T-shaped template [27], and the circular template [28]. For example, Zhang et al [26] manually input three points on both sides of the road, constructed a rectangular template and input direction parameters, selected the grey difference and Euclidean distance as similarity measures, determined the optimal tracking point, and extracted the road using an iterative method.…”

Section: Introductionmentioning

confidence: 99%

Road Extraction From High-Resolution Satellite Images Based on Multiple Descriptors

Dai

Zhu

Wang

et al. 2020

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Geometry and texture noise make it difficult to accurately describe road image rules, which leads to the low degree of automation of traditional template matching algorithms based on internal texture homogenization. We propose a semi-automatic road extraction method based on multiple descriptors to improve the degree of automation while ensuring the accuracy of road extraction. This method aims to address the problems of incomplete road image geometric information and poor homogeneity of internal road texture. The multiscale line segment orientation histogram model and sector descriptor are established. Road points are tracked by interpolation and extension, and postprocessing is used to fit the tracking points and extract the road routes. In this article, high-resolution remote sensing images of different types, different resolutions, and different scenes are selected, and the roads exhibit curvatures, vehicle and shadow occlusions, roundabouts, and variational features. Experiments show that for roads with a certain width, completeness, and correctness of the method are more than 98%. Additionally, as compared with other algorithms, the interactive human intervention of this method is reduced by more than 2/3.

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“…Template matching refers to recognizing predefined template images in a source image. It plays an important role in face recognition [1], pulmonary nodules detection [2], handwriting identification [3], and road detection [4] over the past few decades.…”

Section: Introductionmentioning

confidence: 99%

Template matching using grey wolf optimizer with lateral inhibition

Zhang

Zhou

2017

Optik

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In this paper, a hybrid method of grey wolf optimizer (GWO) and lateral inhibition (LI) is proposed to solve complicated template matching problems. The proposed template matching technique is called LI-GWO. GWO is a new meta-heuristic algorithm inspired by the hunting behavior and social leadership of grey wolves in nature. In addition, lateral inhibition mechanism has been verified to have good effects on image edge extraction and image enhancement. So we employ lateral inhibition for image pre-processing. LI-GWO combines both advantages of GWO and literal inhibition and makes better performance. Series of comparative experimental results show that the proposed method achieves the best balance in comparison to other algorithms based on lateral inhibition in terms of estimation accuracy and the computational cost.

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Road Detection from Optical Remote Sensing Imagery Using Circular Projection Matching and Tracking Strategy

Cited by 19 publications

References 19 publications

Road Extraction Methods in High-Resolution Remote Sensing Images: A Comprehensive Review

Road Extraction Methods in High-Resolution Remote Sensing Images: A Comprehensive Review

Road Extraction From High-Resolution Satellite Images Based on Multiple Descriptors

Template matching using grey wolf optimizer with lateral inhibition

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