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.

中文翻译：

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基于聚类中心偏移的SAR图像船舶目标分割

船舶目标分割在合成孔径雷达（SAR）图像解译中发挥着重要作用。然而，现有的海洋 SAR 图像分割方法存在边缘分割不准确的问题，这是现实世界应用的一个问题。在这封信中，我们提出了一种聚类中心偏移自适应目标分割（CCSATS）方法。首先，提出的聚类中心偏移方法用于更新每次迭代的聚类中心，可以快速准确地捕获船舶像素。然后，基于区域同质性系数，我们定义了一个具有两个自适应权重因子的新的相似性度量标准，以确保分割结果的同质性。最后，邻域块用于表示像素信息，可以减少散斑噪声的影响，增强目标边缘拟合能力。我们对实测SAR图像的分割结果表明，该方法有效地保证了分割精度（SA）。与其他现有方法相比，所提出的目标分割方法实现了更好的边缘捕获性能。