• DocumentCode
    619230
  • Title

    Support vector machine classification to detect land cover changes in Halabja City, Iraq

  • Author

    Al-Doski, Jwan ; Mansor, S.B. ; Shafri, Helmi Zulhaidi Mohd

  • Author_Institution
    Dept. of Civil Eng., Univ. Putra Malaysia (UPM), Serdang, Malaysia
  • fYear
    2013
  • fDate
    7-9 April 2013
  • Firstpage
    353
  • Lastpage
    358
  • Abstract
    Halabja city in Iraq has faced drastic landscape change since the IraqIran war, especially when this city and the surrounding areas were attacked with chemical bombs in 1988. This paper illustrates the results of land use/cover change in Halabja obtained by using multi-temporal remotely sensed data from 1986 to 1990. The support vector machine supervised classification technique was used to extract information from satellite data, and post-classification change detection method was employed to detect and monitor land use/cover change. Derived land use/cover maps were further validated by using high resolution images derived from Google earth. The results from this research indicate that the overall accuracy of land cover maps generated from Landsat Thematic Mapper (TM) data were more than 89%. The urban areas and vegetation classes decreased approximately 58.7% to 40.7% between 1986 and 1990, while bare land increased 25.4%. Also, some changes in urban areas were detected that have already been identified as bombed areas particularly around the main roads of Halabja city.
  • Keywords
    artificial satellites; environmental monitoring (geophysics); geophysical image processing; image classification; image resolution; learning (artificial intelligence); support vector machines; terrain mapping; Google earth; Halabja city; Iraq-Iran war; Landsat TM data; Landsat Thematic Mapper data; bare land; bombed areas; chemical bombs; high-resolution images; information extraction; land cover change detection; land cover change monitoring; land cover maps; land use change detection; land use change monitoring; land use maps; landscape change; multitemporal remotely sensed data; postclassification change detection method; satellite data; support vector machine supervised classification technique; urban areas; vegetation classes; Accuracy; Cities and towns; Earth; Remote sensing; Satellites; Support vector machines; Urban areas; Change Detection; Land Use/Land Cover; Landsat TM; Multi-Temporal; Supervised Classification;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Business Engineering and Industrial Applications Colloquium (BEIAC), 2013 IEEE
  • Conference_Location
    Langkawi
  • Print_ISBN
    978-1-4673-5967-2
  • Type

    conf

  • DOI
    10.1109/BEIAC.2013.6560147
  • Filename
    6560147