Contrast-based SAR ship detection in superpixel level

Ship detection from synthetic aperture radar (SAR) images is one of the crucial issues in maritime surveillance. However, it is very difficult to detect ships by traditional detection methods because of the complex background of high-resolution SAR images. We propose a contrast-based target detection method in superpixel level. Experimental results based on ocean SAR images have shown that the proposed method can obtain stable detection performance both in strong clutter and heterogeneous backgrounds. Meanwhile, it has a low computational complexity compared with some existing detection methods. Firstly, the method introduces superpixel segmentation to partition the SAR image. Then according to the difference in the gray distribution and intensity of the target and clutter superpixels, the difference between the target and clutter is increased based on the weighted information entropy and local contrast of the superpixels to achieve target superpixel detection. Finally, the obtained superpixels of the suspected target are clustered, and false alarms are removed based on the geometric characteristics of the target. The method performs detection at the superpixel level, which can ensure the detection accuracy and improve the detection efficiency.