Computer Optics
RUS  ENG    JOURNALS   PEOPLE   ORGANISATIONS   CONFERENCES   SEMINARS   VIDEO LIBRARY   PACKAGE AMSBIB  
General information
Latest issue
Archive

Search papers
Search references

RSS
Latest issue
Current issues
Archive issues
What is RSS



Computer Optics:
Year:
Volume:
Issue:
Page:
Find






Personal entry:
Login:
Password:
Save password
Enter
Forgotten password?
Register


Computer Optics, 2020, Volume 44, Issue 6, paper published in the English version journal
DOI: https://doi.org/10.18287/2412-6179-CO-703
(Mi co868)
 

This article is cited in 6 scientific papers (total in 6 papers)

IMAGE PROCESSING, PATTERN RECOGNITION

Building detection by local region features in SAR images

Sh. Yeab, Ch. X. Chena, A. Nedzvedzc, J. Jiangd

a College of Information Science and Technology, Zhejiang Shuren University, Zhejiang, China
b School of Earth Sciences, Zhejiang University, Zhejiang, China
c Department of Computer Applications and Systems, Belarusian State University, Minsk, Belarus
d College of Information Science and Electronic Engineering, Zhejiang University, Zhejiang, China
References:
Abstract: The buildings are very complex for detection on SAR images, where the basic features of those are shadows. There are many different representations for SAR shadow. As result it is no possible to use convolutional neural network for building detection directly. In this article we give property analysis of SAR shadows of different type buildings. After that, each region (ROI) prepared for training of building detection is corrected with its own SAR shadow properties. Reconstructions of ROI will be put in a modified YOLO network for building detection with better quality result.
Keywords: SAR images, building detection, YOLO network.
Funding agency Grant number
Zhejiang Provincial Natural Science Foundation of China LGJ18F020001
LGF18F030004
LGJ19F020002
LGF19F020016
G20200216025
100
BRFFI F18R-218
The work was partially funded by Public Welfare Technology Applied Research Program of Zhejiang Province under Grant (No.LGJ18F020001, LGF18F030004, LGJ19F020002 , and LGF19F020016), and by National introduction project of senior foreign experts under Grant No.G20200216025. Introduction Project of Zhejiang Province under Grant (No.100), and project of BRFFI F18R-218 "Development and experimental research of descriptive methods for automatization of biomedical images analysis".
Received: 14.02.2020
Accepted: 17.10.2020
Document Type: Article
Language: Russian
Citation: Sh. Ye, Ch. X. Chen, A. Nedzvedz, J. Jiang
Citation in format AMSBIB
\Bibitem{YeCheNed20}
\by Sh.~Ye, Ch.~X.~Chen, A.~Nedzvedz, J.~Jiang
\mathnet{http://mi.mathnet.ru/co868}
\crossref{https://doi.org/10.18287/2412-6179-CO-703}
Linking options:
  • https://www.mathnet.ru/eng/co868
  • This publication is cited in the following 6 articles:
    Citing articles in Google Scholar: Russian citations, English citations
    Related articles in Google Scholar: Russian articles, English articles
    Computer Optics
    Statistics & downloads:
    Abstract page:59
    Full-text PDF :22
    References:10
     
      Contact us:
     Terms of Use  Registration to the website  Logotypes © Steklov Mathematical Institute RAS, 2024