Performance Comparison of Statistical Models for Characterizing Sea Clutter and Ship CFAR Detection in SAR Images - Sheng Gao; Hongli Liu (IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2022)
A fundamental issue of maritime applications of synthetic aperture radar (SAR) data is the development of precise statistical models for clutter pixels. Several statistical models including the GK, K+R, and ${\mathcal{G}}_{\text{AO}}$ have been demonstrated to be promising for characterizing sea clutter in SAR images. This article is devoted to investigating the improvements in clutter fitting and ship detection performances by using the recently proposed ${\mathcal{G}}_{\text{AO}}$ , compared to that using the GK and K+R. First, the solution uniqueness of parameter estimators by applying the “method of log cumulants” for the ${\mathcal{G}}_{\text{AO}}$ is mathematically proven in the first time. Then, we assess the fitting performance of different models for sea surfaces with different wind speed conditions. Next, the constant false alarm rate (CFAR) detection performance of ships based on different models is compared by the indicators of CFAR loss and detection efficiency. Experiments performed on L-band ALOS-PALSAR SAR data verify the modeling capability of the ${\mathcal{G}}_{\text{AO}}$ model for sea clutter. Moreover, several ship detection examples indicate the usefulness and potential of the ${\mathcal{G}}_{\text{AO}}$ model for CFAR detection in practical applications.