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PolSAR image segmentation based on hierarchical region merging and segment refinement with WMRF model

机译：基于分层区域合并和WMRF模型细分的PolSAR图像分割

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In this paper, a superpixel-based segmentation method is proposed for PolSAR images by utilizing hierarchical region merging and segment refinement. The loss of the energy function, which determines the consistency of two adjacent regions from the statistical aspect, is applied to guide the merging procedure. In addition to the edge penalty term, the homogeneity measurement is also employed to prevent merging the regions that are from different land covers or objects. Based on the merged segments, the segment refinement is applied to further improve the segmentation accuracy by iteratively relabeling the edge pixels. It uses a maximum a posterior (MAP) criterion using the statistical distribution of the pixels and the Markov random field (MRF) model. The performance of the proposed method is validated on an experimental PolSAR dataset from the ESAR system.
机译：本文提出了一种基于超像素的PolSAR图像分割方法。从统计角度确定两个相邻区域的一致性的能量函数损失可用于指导合并过程。除了边缘罚分项外，还使用同质性度量来防止合并来自不同土地覆被或物体的区域。基于合并的段，通过迭代地重新标记边缘像素，应用段细化以进一步提高分割精度。它使用最大后验（MAP）标准，该标准使用像素的统计分布和马尔可夫随机场（MRF）模型。在ESAR系统的实验PolSAR数据集上验证了该方法的性能。

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