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C-UNet: Complement UNet for Remote Sensing Road Extraction

Roads are important mode of transportation, which are very convenient for people’s daily work and life. However, it is challenging to accuratly extract road information from a high-resolution remote sensing image. This paper presents a road extraction method for remote sensing images with a compleme...

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Detalles Bibliográficos
Autores principales: Hou, Yuewu, Liu, Zhaoying, Zhang, Ting, Li, Yujian
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8003503/
https://www.ncbi.nlm.nih.gov/pubmed/33808588
http://dx.doi.org/10.3390/s21062153
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author Hou, Yuewu
Liu, Zhaoying
Zhang, Ting
Li, Yujian
author_facet Hou, Yuewu
Liu, Zhaoying
Zhang, Ting
Li, Yujian
author_sort Hou, Yuewu
collection PubMed
description Roads are important mode of transportation, which are very convenient for people’s daily work and life. However, it is challenging to accuratly extract road information from a high-resolution remote sensing image. This paper presents a road extraction method for remote sensing images with a complement UNet (C-UNet). C-UNet contains four modules. Firstly, the standard UNet is used to roughly extract road information from remote sensing images, getting the first segmentation result; secondly, a fixed threshold is utilized to erase partial extracted information; thirdly, a multi-scale dense dilated convolution UNet (MD-UNet) is introduced to discover the complement road areas in the erased masks, obtaining the second segmentation result; and, finally, we fuse the extraction results of the first and the third modules, getting the final segmentation results. Experimental results on the Massachusetts Road dataset indicate that our C-UNet gets the higher results than the state-of-the-art methods, demonstrating its effectiveness.
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spelling pubmed-80035032021-03-28 C-UNet: Complement UNet for Remote Sensing Road Extraction Hou, Yuewu Liu, Zhaoying Zhang, Ting Li, Yujian Sensors (Basel) Article Roads are important mode of transportation, which are very convenient for people’s daily work and life. However, it is challenging to accuratly extract road information from a high-resolution remote sensing image. This paper presents a road extraction method for remote sensing images with a complement UNet (C-UNet). C-UNet contains four modules. Firstly, the standard UNet is used to roughly extract road information from remote sensing images, getting the first segmentation result; secondly, a fixed threshold is utilized to erase partial extracted information; thirdly, a multi-scale dense dilated convolution UNet (MD-UNet) is introduced to discover the complement road areas in the erased masks, obtaining the second segmentation result; and, finally, we fuse the extraction results of the first and the third modules, getting the final segmentation results. Experimental results on the Massachusetts Road dataset indicate that our C-UNet gets the higher results than the state-of-the-art methods, demonstrating its effectiveness. MDPI 2021-03-19 /pmc/articles/PMC8003503/ /pubmed/33808588 http://dx.doi.org/10.3390/s21062153 Text en © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Hou, Yuewu
Liu, Zhaoying
Zhang, Ting
Li, Yujian
C-UNet: Complement UNet for Remote Sensing Road Extraction
title C-UNet: Complement UNet for Remote Sensing Road Extraction
title_full C-UNet: Complement UNet for Remote Sensing Road Extraction
title_fullStr C-UNet: Complement UNet for Remote Sensing Road Extraction
title_full_unstemmed C-UNet: Complement UNet for Remote Sensing Road Extraction
title_short C-UNet: Complement UNet for Remote Sensing Road Extraction
title_sort c-unet: complement unet for remote sensing road extraction
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8003503/
https://www.ncbi.nlm.nih.gov/pubmed/33808588
http://dx.doi.org/10.3390/s21062153
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