SEA clutter modeling by statistical majority consistency for ship detection in SAR imagery

Probability density function (pdf) estimation of sea clutter in synthetic aperture radar (SAR) imagery has a fundamental role in constructing a constant false alarm rate (CFAR) based ship detector. This paper proposes a semi-parametric sea clutter modeling method for SAR amplitude imagery. The pdf of sea clutter is estimated point by point for each amplitude value, by selecting an optimal component from a given dictionary. For a specific point, the optimal component is selected by measuring the statistical consistency between pdfs of different components and the pdf of sample data within a local window in pdf domain. The statistical consistency is measured by Kullback-Leibler distance (KL-distance). The size of local window is determined based on smoothness criterion. Experimental results on several real SAR imageries demonstrate that the proposed method accurately models the sea clutter, and is flexible to combine with CFAR to construct a ship detector.