• DocumentCode
    45297
  • Title

    Ship Detection for High-Resolution SAR Images Based on Feature Analysis

  • Author

    Chao Wang ; Shaofeng Jiang ; Hong Zhang ; Fan Wu ; Bo Zhang

  • Author_Institution
    Key Lab. of Digital Earth, Inst. of Remote Sensing & Digital Earth, Beijing, China
  • Volume
    11
  • Issue
    1
  • fYear
    2014
  • fDate
    Jan. 2014
  • Firstpage
    119
  • Lastpage
    123
  • Abstract
    High-resolution synthetic aperture radar (SAR) data have been widely used in marine environmental protection, marine environmental monitoring, and marine traffic management. Ship detection is one of the important parts of SAR data for marine applications. This letter focuses on the feature analysis of ships in high-resolution SAR images and proposes an improved optimizing algorithm for ship detection. A fast block detector is designed to extract sea clutter in a uniform local area, and then a constant false alarm rate detector is employed. Based on the kernel density estimation of ships, aspect ratio, and pixel points, ships are identified. TerraSAR-X and COSMO-SkyMed images are used to test our algorithm. The experimental results show that this algorithm can be implemented with time-saving, high-precision ship extraction, feature analysis, and detection.
  • Keywords
    feature extraction; image resolution; marine pollution; marine radar; object detection; optimisation; radar clutter; radar imaging; ships; synthetic aperture radar; COSMO-SkyMed image; SAR image high resolution; TerraSAR-X; aspect ratio; constant false alarm rate detector; fast block detector; feature analysis; kernel density estimation; marine environmental monitoring; marine environmental protection; marine traffic management; optimization algorithm; pixel point; sea clutter extraction; ship detection; synthetic aperture radar; Conventional constant false alarm rate (CFAR); feature analysis; high-resolution synthetic aperture radar (SAR); kernel density estimation; ship detection;
  • fLanguage
    English
  • Journal_Title
    Geoscience and Remote Sensing Letters, IEEE
  • Publisher
    ieee
  • ISSN
    1545-598X
  • Type

    jour

  • DOI
    10.1109/LGRS.2013.2248118
  • Filename
    6512556