• DocumentCode
    2828738
  • Title

    Fire scene segmentations for forest fire characterization: A comparative study

  • Author

    Collumeau, Jean-François ; Laurent, Hélène ; Hafiane, Adel ; Chetehouna, Khaled

  • Author_Institution
    Lab. PRISME UPRES EA 4229 88, ENSI de Bourges, Bourges, France
  • fYear
    2011
  • fDate
    11-14 Sept. 2011
  • Firstpage
    2973
  • Lastpage
    2976
  • Abstract
    Forest fires constitute one of the most damaging natural disaster for many countries around the world. Its mechanisms can be studied through forest fire metrology. Despite a large number of proposed algorithms for fire detection, only few works adress the segmentation problem in fire metrology. The main purpose of this paper is to introduce a SVM-based segmentation method for forest fire metrology. This method is confronted with state-of-the-art fire segmentation algorithms using supervised evaluation criteria and an ad hoc expertised picture database. The obtained results highlight the good performances of the proposed method compared to prior algorithms.
  • Keywords
    disasters; fires; forestry; image segmentation; support vector machines; visual databases; SVM-based segmentation method; ad hoc expertised picture database; fire detection; fire scene segmentation problem; forest fire characterization; forest fire metrology; natural disaster; state-of-the-art fire segmentation algorithm; supervised evaluation criteria; Databases; Fires; Image color analysis; Image segmentation; Measurement; Metrology; Support vector machines; Forest fire metrology; Support Vector Machine; fire segmentation; supervised evaluation;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Image Processing (ICIP), 2011 18th IEEE International Conference on
  • Conference_Location
    Brussels
  • ISSN
    1522-4880
  • Print_ISBN
    978-1-4577-1304-0
  • Electronic_ISBN
    1522-4880
  • Type

    conf

  • DOI
    10.1109/ICIP.2011.6116285
  • Filename
    6116285