Adaptive CFAR Detection of Ship Targets in High Resolution SAR Imagery

Ship detection is significant especially with the increasing worldwide cooperation in commerce and military affairs. Space-borne Synthetic Aperture Radar (SAR) is optimal for ship detection due to its high resolution over wide swaths and all-weather working capability. Constant False Alarm Rate (CFAR) detection of ships in SAR imagery is a robust and popular choice. K distribution has been widely accepted for homogeneous sea clutter modeling. Although localized K-distribution based CFAR detection has been developed to solve the non-homogeneous problem, it is not satisfied under adverse conditions, for example, interference target appears in the background window. In order to overcome its shortcomings, this paper presents an adaptive algorithm to improve the performance. It mainly includes the homogeneity assessment of the local background area and the automatic selection between the localized K-distribution-based CFAR detector and the OS-CFAR detector, which has better detecting performance in non-homogeneous situation. The theory is investigated in detail firstly, and then experiments are carried out and the results illustrate that the novel algorithm outperforms the state-of-art methods especially under complex sea background condition.