Weak target detection in SAR images via improved itti visual saliency model

Since using conventional constant false alarm ratio (CFAR) algorithms for weak target detection in synthetic aperture radar (SAR) images cause lots of false alarms, this paper proposes a novel detection method based on the Itti visual saliency model. The authors first obtain the feature maps from a SAR image including local variance, frequency of intensity values and global contrast. After Gaussian blurring and normalizing, feature maps are merged using a nonlinear strategy at the original image scale to form the saliency map. As detection results, saliency areas are extracted from the saliency map by combining the focus of attention (FOA) metastasis and region growth methods. Compared with other three methods through experiments, the proposed algorithm performs the best both in accuracy and time complexity.