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Building Extraction Using Mask Scoring R-CNN Network

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成果类型:
期刊论文、会议论文
作者:
Hu, Yiwen;Guo, Fenglin*
通讯作者:
Guo, Fenglin
作者机构:
[Hu, Yiwen; Guo, Fenglin] Wuhan Polytech Univ, Sch Math & Comp Sci, Wuhan, Peoples R China.
通讯机构:
[Guo, Fenglin] W
Wuhan Polytech Univ, Sch Math & Comp Sci, Wuhan, Peoples R China.
语种:
英文
关键词:
Building extraction;Deep learning;Instance segmentation
期刊:
PROCEEDINGS OF THE THIRD INTERNATIONAL CONFERENCE ON COMPUTER SCIENCE AND APPLICATION ENGINEERING (CSAE2019)
年:
2019
页码:
Pages 1–5
会议名称:
3rd International Conference on Computer Science and Application Engineering (CSAE)
会议时间:
OCT 22-24, 2019
会议地点:
Sanya, PEOPLES R CHINA
会议主办单位:
[Hu, Yiwen;Guo, Fenglin] Wuhan Polytech Univ, Sch Math & Comp Sci, Wuhan, Peoples R China.
会议赞助商:
Assoc Comp Machinery, Assoc Sci & Engn
主编:
Emrouznejad, A
出版地:
1515 BROADWAY, NEW YORK, NY 10036-9998 USA
出版者:
ASSOC COMPUTING MACHINERY
ISBN:
978-1-4503-6294-8
机构署名:
本校为第一且通讯机构
院系归属:
数学与计算机学院
摘要:
Extracting buildings from high resolution remotely sensed images is very practical, which can be applied to urban modeling and so on. The development of computer vision has become better, and the accuracy of recognition of convolutional neural networks has exceeded the accuracy of recognition of human eyes. In this paper, we used a deep convolutional neural network in remote sensing to achieve building extraction. The method in this paper is not based on semantic segmentation, but instance segmentation, which considered each building as an independent individual to achieve building extraction....

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