In maritime applications involving estimation of radar sea clutter properties, non-sea-clutter targets and transitions between statistically different oceanographic conditions in the estimation window may lead to inaccurate modeling. Referring to mixtures in the estimation window as contamination, this work introduces a novel sea clutter contamination test based on log-cumulants from Mellin kind statistics . It measures the significant deviation in log-cumulant space due to the contamination, and appears to be an effective tool for improving the sea clutter estimation or to be a direct first-stage target detector. The proposed contamination test is examined with real single look complex (SLC) fine resolution quad-polarimetric Radarsat-2 synthetic aperture radar (SAR) measurements, from the Norwegian Sea, under various oceanographic conditions.
This work investigates the fixed-point polarimetric whitening filter (FP-PWF) with respect to ship detection
based on polarimetric synthetic aperture radar (SAR) imagery. The purposes of this work are: (i) to investigate
the FP-PWF algorithm that incorporate texture, (ii) to examine the method of log-cumulants (MoLC) for shape
parameter estimation associated with texture, and (iii) to assess the impact of the improved modeling and estimation
on the discrepancy between specified and observed false alarm rate. A modified ship detection algorithm
based on FP-PWF is proposed with improved modeling, estimation and detection performance. Experiments
are performed on simulated radar ocean clutter.