Ship Target Segmentation for SAR Images Based on Clustering Center Shift

Ship target segmentation plays an important role in synthetic aperture radar (SAR) image interpretation. However, the existing segmentation methods for marine SAR images have the problem of inaccurate edge segmentation, a concern for real-world applications. In this letter, we propose a clustering center-shifted adaptive target segmentation (CCSATS) method. First, the proposed clustering center shift method is used to update the clustering centers of each iteration, which can quickly and accurately capture ship pixels. Then, based on regional homogeneity coefficients, we define a new similarity measurement criterion with two adaptive weight factors to ensure the homogeneity of segmentation results. Finally, neighborhood patches are used to represent pixel information, which can reduce the influence of speckle noise and enhance the target edge fitting ability. Our segmentation results of measured SAR images show that the proposed method effectively ensures segmentation accuracy (SA). Compared with other existing methods, the proposed target segmentation method achieves better edge capture performance.