Statistical Modeling of Shadows in SAR Imagery

Xiaojun Luo, Xiangyang Zhang, Jiawen Bao*, Ling Chang, Weixin Xi

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

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Abstract

Shadows are a special distortion in synthetic aperture radar (SAR) imaging. They often hamper proper image understanding and target recognition but also offer useful information, and therefore, the statistical modeling of SAR image shadows is imperative. In this endeavor, we systematically deduced the statistical models of shadows in multimodal SAR images, including single-look intensity and amplitude images and multilook intensity and amplitude images in a real domain and complex domain, respectively. In particular, for the filtered SAR image shadow, we introduced the generalized extreme value (GEV) distribution to characterize its statistics. We carried out an experiment on shadows in a real SAR image and conducted chi-square goodness-of-fit tests on the deduced models. Furthermore, we compared the deduced statistical models of shadows with state-of-the-art statistical models of SAR imagery. Finally, suggestions are given for selecting the optimal statistical model of shadows in multimodal SAR images.

Original languageEnglish
Article number4437
JournalMathematics
Volume11
Issue number21
DOIs
Publication statusPublished - Nov 2023

Keywords

  • probability density function (PDF)
  • SAR image
  • shadow
  • statistical modeling
  • ITC-GOLD
  • ITC-ISI-JOURNAL-ARTICLE

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