Superpixel-level CFAR Ship Detection Based on Polarimetric Bilateral Truncated Statistics

Constant false alarm rate (CFAR) detector is a common method for ship detection in polarimetric synthetic aperture radar (PolSAR) images. CFAR detectors greatly depend on the clutter modeling that can be easily affected by the contamination caused by both lower- and higher-intensity outliers, such as spilled oil and intensive targets. Traditional CFAR detectors perform detection in a pixel-by-pixel manner, which ignores the spatial information. Both the bias in clutter modeling and the absence of spatial information can degrade the ship target detection performance. In this study, a superpixel-level polarimetric bilateral truncated statistics CFAR detector is proposed to promote the ship target detection performance in complex ocean scenarios. As the preprocessing of the PolSAR image, the superpixel segmentation is conducted based on the multilook polarimetric whitening filter result to select candidate ship target superpixels for bilateral truncation and background clutter modeling. The elliptical truncation is expanded to a complex situation and the relationship between the second moments before and after truncation is derived. The maximum-likelihood estimation estimator of the equivalent number of looks based on the bilateral truncation distribution is derived and compared with other parameter estimators. The influence of the truncation depth on estimator performance is analyzed, according to which the adaptive bilateral truncation method is determined. The Gaussian mixture model and the Parzen window kernel method are compared with the model-based method and utilized for data fitting. The proposed method performs bilateral truncation based on the superpixel segmentation result to provide pure clutter samples for accurate parameter estimation and clutter distribution modeling, reducing time consumption and false alarms. The method is validated efficient on both simulated and measured data from RADARSAT-2.