• DocumentCode
    1797377
  • Title

    A new image fusion strategy based on target segmentaion

  • Author

    Jian-Wei Liu

  • Author_Institution
    Sch. of Sci., Xi´an Technol. Univ., Xi´an, China
  • Volume
    1
  • fYear
    2014
  • fDate
    13-16 July 2014
  • Firstpage
    159
  • Lastpage
    162
  • Abstract
    By analyzing the characteristics of target in multi spectral image and panchromatic image, a new fusion strategy based on target segmentation is proposed to fuse multi spectral image and panchromatic image. Firstly, Hue-Saturation-Intensity (HSI) transform is performed on the multi spectral image. Secondly, the intensity component by HSI transform is segmented by the expectation maximization (EM) algorithm and the panchromatic image is segmented by fuzzy C means (FCM) clustering algorithm, and obtains the better target area, followed by effective filling of the target area to get the new intensity component. Finally, the new fusion image is obtained by inverse HSI transform. Compared with the traditional HSI method, experiment results shows that the proposed strategy not only increases information entropy and average gradient of fusion image, but also decreases spectral bias index. Therefore, the proposed strategy is better than traditional HSI method. It not only enhances spatial resolution of fusion image and obtains detailed and feature information, but also preserves spectral information of the original multi-spectral image well.
  • Keywords
    expectation-maximisation algorithm; image fusion; image segmentation; inverse transforms; EM algorithm; FCM clustering algorithm; expectation maximization algorithm; fuzzy c means clustering algorithm; hue-saturation-intensity transform; image fusion strategy; inverse HSI transform; multi spectral image; panchromatic image; target segmentaion; Abstracts; Entropy; Image resolution; Image segmentation; Expectation maximization; Fuzzy C means clustering; Hue-Saturation-Intensity; Image fusion; Target segmentation;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Machine Learning and Cybernetics (ICMLC), 2014 International Conference on
  • Conference_Location
    Lanzhou
  • ISSN
    2160-133X
  • Print_ISBN
    978-1-4799-4216-9
  • Type

    conf

  • DOI
    10.1109/ICMLC.2014.7009110
  • Filename
    7009110