This paper presents an improved constant false alarm rate (CFAR) model for ship detection in synthetic aperture radar (SAR) imagery. The model includes the probabilistic neural networks, CFAR technique, golden section method and area growth method. It is compared with other ship detection methods. The results show that the improved CFAR model performs well
['Weigen Huang', 'Peng Chen', 'Jingsong Yang', 'Bin Fu', 'Qingmei Xiao', 'Lu Yao', 'Changbao Zhou']
An improved CFAR model for ship detection in SAR imagery