Narrow road extraction from remote sensing images based on super-resolution convolutional neural network

Xinyu Zhou; Xi Chen; Ye Zhang

doi:10.1109/IGARSS.2018.8517851

Narrow road extraction from remote sensing images based on super-resolution convolutional neural network

Xinyu Zhou, Xi Chen, Ye Zhang

Software Engineering Institute

Harbin Institute of Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

2 Scopus citations

Abstract

In remote sensing images, it is usually hard to extract narrow roads with only several pixels width. To address this problem, the original remote sensing images are processed with super-resolution to enlarge the details of the narrow roads by a convolutional neural network method. Then the One-Class Support Vector Machine (OCSVM) classifier is applied after super-resolution for exact extraction of narrow roads. Experiments are conducted on an open dataset of remote sensing images to verify the performance of the new method and the results are compared with the method without image super-resolution. The experimental results demonstrate the validity and superiority of the new method.

Original language	English
Title of host publication	2018 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	685-688
Number of pages	4
ISBN (Electronic)	9781538671504
DOIs	https://doi.org/10.1109/IGARSS.2018.8517851
State	Published - 31 Oct 2018
Event	38th Annual IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 - Valencia, Spain Duration: 22 Jul 2018 → 27 Jul 2018

Publication series

Name	International Geoscience and Remote Sensing Symposium (IGARSS)
Volume	2018-July

Conference

Conference	38th Annual IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018
Country/Territory	Spain
City	Valencia
Period	22/07/18 → 27/07/18

Keywords

Convolutional neural network
Narrow road extraction
Super-resolution

Access to Document

10.1109/IGARSS.2018.8517851

Cite this

Zhou, X., Chen, X., & Zhang, Y. (2018). Narrow road extraction from remote sensing images based on super-resolution convolutional neural network. In 2018 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 - Proceedings (pp. 685-688). Article 8517851 (International Geoscience and Remote Sensing Symposium (IGARSS); Vol. 2018-July). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IGARSS.2018.8517851

Zhou, Xinyu ; Chen, Xi ; Zhang, Ye. / Narrow road extraction from remote sensing images based on super-resolution convolutional neural network. 2018 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2018. pp. 685-688 (International Geoscience and Remote Sensing Symposium (IGARSS)).

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title = "Narrow road extraction from remote sensing images based on super-resolution convolutional neural network",

abstract = "In remote sensing images, it is usually hard to extract narrow roads with only several pixels width. To address this problem, the original remote sensing images are processed with super-resolution to enlarge the details of the narrow roads by a convolutional neural network method. Then the One-Class Support Vector Machine (OCSVM) classifier is applied after super-resolution for exact extraction of narrow roads. Experiments are conducted on an open dataset of remote sensing images to verify the performance of the new method and the results are compared with the method without image super-resolution. The experimental results demonstrate the validity and superiority of the new method.",

keywords = "Convolutional neural network, Narrow road extraction, Super-resolution",

author = "Xinyu Zhou and Xi Chen and Ye Zhang",

note = "Publisher Copyright: {\textcopyright} 2018 IEEE; 38th Annual IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 ; Conference date: 22-07-2018 Through 27-07-2018",

year = "2018",

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doi = "10.1109/IGARSS.2018.8517851",

language = "英语",

series = "International Geoscience and Remote Sensing Symposium (IGARSS)",

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booktitle = "2018 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 - Proceedings",

address = "美国",

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Zhou, X, Chen, X & Zhang, Y 2018, Narrow road extraction from remote sensing images based on super-resolution convolutional neural network. in 2018 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 - Proceedings., 8517851, International Geoscience and Remote Sensing Symposium (IGARSS), vol. 2018-July, Institute of Electrical and Electronics Engineers Inc., pp. 685-688, 38th Annual IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018, Valencia, Spain, 22/07/18. https://doi.org/10.1109/IGARSS.2018.8517851

Narrow road extraction from remote sensing images based on super-resolution convolutional neural network. / Zhou, Xinyu; Chen, Xi; Zhang, Ye.
2018 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2018. p. 685-688 8517851 (International Geoscience and Remote Sensing Symposium (IGARSS); Vol. 2018-July).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Narrow road extraction from remote sensing images based on super-resolution convolutional neural network

AU - Zhou, Xinyu

AU - Chen, Xi

AU - Zhang, Ye

PY - 2018/10/31

Y1 - 2018/10/31

N2 - In remote sensing images, it is usually hard to extract narrow roads with only several pixels width. To address this problem, the original remote sensing images are processed with super-resolution to enlarge the details of the narrow roads by a convolutional neural network method. Then the One-Class Support Vector Machine (OCSVM) classifier is applied after super-resolution for exact extraction of narrow roads. Experiments are conducted on an open dataset of remote sensing images to verify the performance of the new method and the results are compared with the method without image super-resolution. The experimental results demonstrate the validity and superiority of the new method.

AB - In remote sensing images, it is usually hard to extract narrow roads with only several pixels width. To address this problem, the original remote sensing images are processed with super-resolution to enlarge the details of the narrow roads by a convolutional neural network method. Then the One-Class Support Vector Machine (OCSVM) classifier is applied after super-resolution for exact extraction of narrow roads. Experiments are conducted on an open dataset of remote sensing images to verify the performance of the new method and the results are compared with the method without image super-resolution. The experimental results demonstrate the validity and superiority of the new method.

KW - Convolutional neural network

KW - Narrow road extraction

KW - Super-resolution

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U2 - 10.1109/IGARSS.2018.8517851

DO - 10.1109/IGARSS.2018.8517851

M3 - 会议稿件

AN - SCOPUS:85063131462

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SP - 685

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BT - 2018 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 - Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 38th Annual IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018

Y2 - 22 July 2018 through 27 July 2018

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Zhou X, Chen X, Zhang Y. Narrow road extraction from remote sensing images based on super-resolution convolutional neural network. In 2018 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2018. p. 685-688. 8517851. (International Geoscience and Remote Sensing Symposium (IGARSS)). doi: 10.1109/IGARSS.2018.8517851

Narrow road extraction from remote sensing images based on super-resolution convolutional neural network

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