• DocumentCode
    3058816
  • Title

    Statistical analysis and modeling of TerraSAR-X images for CFAR based target detection

  • Author

    Ni Weiping ; Yan Weidong ; Wu Junzheng ; Zheng Gang ; Lu Ying

  • Author_Institution
    Northwest Inst. of Nucl. Technol., Xian, China
  • fYear
    2013
  • fDate
    21-26 July 2013
  • Firstpage
    1983
  • Lastpage
    1986
  • Abstract
    TerraSAR-X is the first commercially operated high resolution space-borne SAR in the world, which shows good potential in target detection fields. Statistical analysis and modeling are key steps in SAR images based automatic target detection systems. With four typical statistical measures, lognormal distribution is proved well to fit the histograms of TerraSAR-X images over land and ocean regions, and more suitable than Weibull, Gamma, K, G0 and α stable distributions to modeling statistics of such images. Additionally, the MLE based parameters estimation and simply analytical expression of detection threshold also indicates the effectiveness and efficiency of lognormal based CFAR for target detection.
  • Keywords
    data analysis; geophysical image processing; log normal distribution; maximum likelihood estimation; object detection; radar imaging; remote sensing by radar; spaceborne radar; synthetic aperture radar; CFAR based target detection; MLE based parameter estimation; TerraSAR-X image modeling; TerraSAR-X image statistical analysis; automatic target detection systems; detection threshold; high resolution spaceborne SAR; log-normal distribution; statistical measures; target detection fields; Analytical models; Histograms; Image resolution; Object detection; Oceans; Statistical analysis; Synthetic aperture radar; MSTAR image; combined thresholding rules; image segmentation; moment feature transformation;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Geoscience and Remote Sensing Symposium (IGARSS), 2013 IEEE International
  • Conference_Location
    Melbourne, VIC
  • ISSN
    2153-6996
  • Print_ISBN
    978-1-4799-1114-1
  • Type

    conf

  • DOI
    10.1109/IGARSS.2013.6723197
  • Filename
    6723197