The objective of this article is to investigate a multiphase segmentation framework for synthetic aperture radar (SAR) images, which is proposed based on the idea of the fuzzy region competition-based method. The fuzzy region competition-based framework is highly efficient and can attain good segmentation performances for conventional images. The framework is achieved based on its convexity, which not only ensures the existence of a globally optimized solution but also enables the convex optimization theory-based solving algorithms that are feasible. However, the constraint conditions of the framework that guarantee this convexity probably cannot be satisfied in the segmentation of images corrupted with strong noise. Therefore, applying this method to an SAR image probably produces an unsatisfactory segmentation result. To address this problem, we propose an improved fuzzy region competition-based framework in terms of the hierarchical strategy, such that the framework is always convex during the iterative calculation. The proposed framework inherits the advantages of the fuzzy region competition-based method, as well as that it is able to be applied to the segmentation of images with strong noise. Several experiments are then carried out to test and verify the performance and the robustness of the proposed framework. It demonstrates that the proposed segmentation framework can be applied to various types of SAR images and achieves satisfactory segmentation results.

中文翻译：

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一种改进的基于模糊区域竞争的 SAR 图像多相分割框架

本文的目的是研究基于模糊区域竞争方法的思想提出的合成孔径雷达 (SAR) 图像的多相分割框架。基于模糊区域竞争的框架效率很高，可以对传统图像获得良好的分割性能。该框架是基于其凸性实现的，不仅保证了全局优化解的存在，而且使得基于凸优化理论的求解算法可行。然而，在被强噪声破坏的图像分割中，保证这种凸性的框架的约束条件可能无法满足。因此，将此方法应用于 SAR 图像可能会产生不令人满意的分割结果。为了解决这个问题，我们在分层策略方面提出了一种改进的基于模糊区域竞争的框架，使得该框架在迭代计算过程中始终是凸的。所提出的框架继承了基于模糊区域竞争的方法的优点，并且能够应用于强噪声图像的分割。然后进行了几个实验来测试和验证所提出框架的性能和鲁棒性。它表明所提出的分割框架可以应用于各种类型的 SAR 图像并取得令人满意的分割结果。所提出的框架继承了基于模糊区域竞争的方法的优点，并且能够应用于强噪声图像的分割。然后进行了几个实验来测试和验证所提出框架的性能和鲁棒性。它表明所提出的分割框架可以应用于各种类型的 SAR 图像并取得令人满意的分割结果。所提出的框架继承了基于模糊区域竞争的方法的优点，并且能够应用于强噪声图像的分割。然后进行了几个实验来测试和验证所提出框架的性能和鲁棒性。它表明所提出的分割框架可以应用于各种类型的 SAR 图像并取得令人满意的分割结果。