This article proposes an efficient and adaptive statistical superpixel merging approach with edge penalty for polarimetric synthetic aperture radar (PolSAR) image segmentation. Based on the initial superpixel over-segmentation result obtained by our previously proposed adaptive polarimetric superpixel generation algorithm (Pol-ASLIC), this work achieves efficient and accurate PolSAR image segmentation by merging superpixels using the statistical region merging (SRM) framework. This article proposes to define a new dissimilarity measure between superpixels, which takes the edge penalty into consideration, leading to a reasonable and accurate merging order for superpixel pairs. With regard to the merging predicate of superpixels, a polarimetric homogeneity measurement (HoM) is used to define the merging threshold, making the merging predicate and merging threshold adaptive to the PolSAR image content. Experimental results on three airborne and one spaceborne PolSAR data sets demonstrate that the proposed approach can effectively improve the computation efficiency and segmentation accuracy in comparison with state-of-the-art merging-based methods for PolSAR data. More importantly, the proposed approach is free of parameters and easy to use.

中文翻译：

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用于 PolSAR 图像分割的具有边缘惩罚的自适应统计超像素合并

本文提出了一种具有边缘惩罚的高效自适应统计超像素合并方法，用于极化合成孔径雷达 (PolSAR) 图像分割。基于我们之前提出的自适应极化超像素生成算法 (Pol-ASLIC) 获得的初始超像素过分割结果，这项工作通过使用统计区域合并 (SRM) 框架合并超像素来实现高效准确的 PolSAR 图像分割。本文提出定义一种新的超像素之间的相异性度量，该度量将边缘惩罚考虑在内，从而为超像素对提供合理且准确的合并顺序。关于超像素的合并谓词，使用极化均匀性测量（HoM）来定义合并阈值，使合并谓词和合并阈值适应 PolSAR 图像内容。在三个机载和一个星载 PolSAR 数据集上的实验结果表明，与最先进的基于合并的 PolSAR 数据方法相比，所提出的方法可以有效提高计算效率和分割精度。更重要的是，所提出的方法没有参数且易于使用。