Statistical Models for Constant False Alarm Rate Ship Detection with the Subaperture Cross-correlation Magnitude

This paper presents statistical models for the subaperture cross-correlation magnitude (SCM), a test statistic for ship detection which can be produced from single-look complex (SLC) synthetic aperture radar (SAR) data. The SCM is extracted from the complex cross-correlation between two subaperture images and provides enhanced contrast between coherent structures, such as marine vessels, and sea clutter. A modified SCM algorithm has been proposed, which introduces an antialiasing filter in order to allow overlapping sublook spectra. The consequences for the statistical modelling are discussed. We perform an empirical study which validates the use of the K distribution and the Fisher distribution as probability density functions for sea clutter in SCM images. This lays the groundwork for constant false alarm rate (CFAR) detection with SCM images. The fit of the models are assessed with real data.