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

  • School of Electronics and Information Engineering, Harbin Institute of Technology

科研成果: 书/报告/会议事项章节会议稿件同行评审

摘要

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.

源语言英语
主期刊名2018 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 - Proceedings
出版商Institute of Electrical and Electronics Engineers Inc.
685-688
页数4
ISBN(电子版)9781538671504
DOI
出版状态已出版 - 31 10月 2018
活动38th Annual IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 - Valencia, 西班牙
期限: 22 7月 201827 7月 2018

出版系列

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

会议

会议38th Annual IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018
国家/地区西班牙
Valencia
时期22/07/1827/07/18

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