A Density Clustering-Based CFAR Algorithm for Ship Detection in SAR Images

The clutter selection strategy based on sliding window in the conventional constant false alarm rate (CFAR) algorithm leads to different clutter qualities between pixels of the same target in a complex environment. To solve the problem, this letter proposes an improved CFAR algorithm based on density clustering. First, a two-parameter CFAR is used to detect ship targets. Then, density clustering is performed on each detected target pixel based on spatial distance and detection threshold to improve the target detection accuracy. Finally, false alarms caused by speckle noise are eliminated by using the number of times a pixel is clustered. The experimental results show that compared with the conventional CFAR algorithm and the superpixel-level CFAR detectors for ship detection in synthetic aperture radar (SAR) imagery (SP-CFAR), the proposed algorithm achieves a detection accuracy improvement of over 14.8% in heterogeneous clutter scenarios and dense target scenarios, while maintaining a low false alarm rate no higher than 0.13% in strong noise environments.