A target detection algorithm for SAR images based on regional probability statistics and saliency analysis

In comparison with optical images, a Synthetic Aperture Radar (SAR) image has many defects, such as low resolution, strong noise interference and random distribution of the target, which increases the false alarm rate of traditional detection methods. To improve the detection accuracy of the SAR image, a novel detection method is proposed based on regional probability statistics and saliency analysis. A saliency analysis model based on dense and sparse reconstruction (DSR) is reconstructed to locate the target precisely. Firstly, the regional probability of the SAR image is estimated to extract the background region. And then, the extracted background sub-blocks are clustered and employed to replace the corresponding background template set of the DSR model. Subsequently, the reconstructed DSR model is used to extract the target, and the detection accuracy of the proposed method is enhanced greatly. Compared with the constant false alarm rate (CFAR)-based detection method, the proposed method can achieve a high detection accuracy and protect the edges of the SAR image.