Semi-Automatic Extraction of Rural Roads under the Constraint of Combined Geometric and Texture Features

The extraction of road information from high-resolution remotely-sensed images has important application value in many fields. Rural roads have the characteristics of relatively narrow widths and diversified pavement materials; these characteristics can easily lead to problems involving the similari...

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Autores principales:	Hai Tan, Zimo Shen, Jiguang Dai
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	rural roads geometric features texture features semi-automatic extraction Geography (General) G1-922
Acceso en línea:	https://doaj.org/article/270d498f50564a56acdf0a264503f57b
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id	oai:doaj.org-article:270d498f50564a56acdf0a264503f57b
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spelling	oai:doaj.org-article:270d498f50564a56acdf0a264503f57b2021-11-25T17:53:00ZSemi-Automatic Extraction of Rural Roads under the Constraint of Combined Geometric and Texture Features10.3390/ijgi101107542220-9964https://doaj.org/article/270d498f50564a56acdf0a264503f57b2021-11-01T00:00:00Zhttps://www.mdpi.com/2220-9964/10/11/754https://doaj.org/toc/2220-9964The extraction of road information from high-resolution remotely-sensed images has important application value in many fields. Rural roads have the characteristics of relatively narrow widths and diversified pavement materials; these characteristics can easily lead to problems involving the similarity of the road texture with the texture of surrounding objects and make it difficult to improve the automation of traditional high-precision road extraction methods. Based on this background, a semi-automatic rural road extraction method constrained by a combination of geometric and texture features is proposed in this paper. First, an adaptive road width extraction model is proposed to improve the accuracy of the initial road centre point. Then, aiming at the continuous change of curvature of rural roads, a tracking direction prediction model is proposed. Finally, a matching model under geometric texture constraints is proposed, which solves the problem of similarity between road and neighbourhood texture to a certain extent. The experimental results show that by selecting different types of experimental scenes or remotely sensed image data, compared with other methods, the proposed method can not only guarantee the road extraction accuracy but also improve the degree of automation to a certain extent.Hai TanZimo ShenJiguang DaiMDPI AGarticlerural roadsgeometric featurestexture featuressemi-automatic extractionGeography (General)G1-922ENISPRS International Journal of Geo-Information, Vol 10, Iss 754, p 754 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	rural roads geometric features texture features semi-automatic extraction Geography (General) G1-922
spellingShingle	rural roads geometric features texture features semi-automatic extraction Geography (General) G1-922 Hai Tan Zimo Shen Jiguang Dai Semi-Automatic Extraction of Rural Roads under the Constraint of Combined Geometric and Texture Features
description	The extraction of road information from high-resolution remotely-sensed images has important application value in many fields. Rural roads have the characteristics of relatively narrow widths and diversified pavement materials; these characteristics can easily lead to problems involving the similarity of the road texture with the texture of surrounding objects and make it difficult to improve the automation of traditional high-precision road extraction methods. Based on this background, a semi-automatic rural road extraction method constrained by a combination of geometric and texture features is proposed in this paper. First, an adaptive road width extraction model is proposed to improve the accuracy of the initial road centre point. Then, aiming at the continuous change of curvature of rural roads, a tracking direction prediction model is proposed. Finally, a matching model under geometric texture constraints is proposed, which solves the problem of similarity between road and neighbourhood texture to a certain extent. The experimental results show that by selecting different types of experimental scenes or remotely sensed image data, compared with other methods, the proposed method can not only guarantee the road extraction accuracy but also improve the degree of automation to a certain extent.
format	article
author	Hai Tan Zimo Shen Jiguang Dai
author_facet	Hai Tan Zimo Shen Jiguang Dai
author_sort	Hai Tan
title	Semi-Automatic Extraction of Rural Roads under the Constraint of Combined Geometric and Texture Features
title_short	Semi-Automatic Extraction of Rural Roads under the Constraint of Combined Geometric and Texture Features
title_full	Semi-Automatic Extraction of Rural Roads under the Constraint of Combined Geometric and Texture Features
title_fullStr	Semi-Automatic Extraction of Rural Roads under the Constraint of Combined Geometric and Texture Features
title_full_unstemmed	Semi-Automatic Extraction of Rural Roads under the Constraint of Combined Geometric and Texture Features
title_sort	semi-automatic extraction of rural roads under the constraint of combined geometric and texture features
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/270d498f50564a56acdf0a264503f57b
work_keys_str_mv	AT haitan semiautomaticextractionofruralroadsundertheconstraintofcombinedgeometricandtexturefeatures AT zimoshen semiautomaticextractionofruralroadsundertheconstraintofcombinedgeometricandtexturefeatures AT jiguangdai semiautomaticextractionofruralroadsundertheconstraintofcombinedgeometricandtexturefeatures
_version_	1718411852739772416

Semi-Automatic Extraction of Rural Roads under the Constraint of Combined Geometric and Texture Features

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