• DocumentCode
    2462755
  • Title

    Extract agricultural land use information from remote sensing image based on object-oriented classification algorithm

  • Author

    Chen, Qiang ; Yin, Qiu

  • Author_Institution
    Shanghai Inst. of Tech. Phys., Shanghai, China
  • fYear
    2011
  • fDate
    24-26 June 2011
  • Firstpage
    4983
  • Lastpage
    4986
  • Abstract
    On the research of agricultural Non-point source pollution, agricultural land use data is the most important basic information, it´s hard to obtain by statistical means and it update frequently. By the help of remote sensing image and information extraction techniques, agricultural land use information can be extracted with low-cost and fast and accurately. In this study, we established index system of agricultural land use classification in line with agricultural non-point source pollution, and use the Japanese ALOS satellite as a data source, extract agricultural land use information of Chongming Island by object-oriented remote sensing image classification algorithm. After a wild field verification, classification accuracy can meet agricultural nonpoint source pollution requirements.
  • Keywords
    agricultural pollution; geophysical image processing; image classification; object-oriented methods; remote sensing; Japanese ALOS satellite; agricultural land use information extraction; agricultural nonpoint source pollution requirements; data source; index system; object-oriented image classification algorithm; remote sensing image; statistical means; wild field verification; Accuracy; Classification algorithms; Data mining; Image classification; Pollution; Remote sensing; Satellites; agricultural land use; classification accuracy verification; object-oriented classification algorithm; remote sensing image;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Remote Sensing, Environment and Transportation Engineering (RSETE), 2011 International Conference on
  • Conference_Location
    Nanjing
  • Print_ISBN
    978-1-4244-9172-8
  • Type

    conf

  • DOI
    10.1109/RSETE.2011.5965431
  • Filename
    5965431