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首页> 外文期刊>IEEE Geoscience and Remote Sensing Letters >Edge-Aware Superpixel Generation for SAR Imagery With One Iteration Merging
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Edge-Aware Superpixel Generation for SAR Imagery With One Iteration Merging

机译：SAR图像的边缘感知超像素生成，具有一次迭代合并

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摘要

Most of the existing superpixel generation methods are based on local iterative clustering. However, such methods have the following shortcomings: 1) these methods require several iterations and the number of iterations is difficult to determine and 2) the generated superpixel lacks explicit connectivity without a postprocessing step. Aiming to overcome the limitations, we propose an edge-aware superpixel generation with one iteration merging (ESOM) for synthetic aperture radar (SAR) imagery. In specific, we introduce a ratio-based edge detector with a Gaussian-shaped window to extract the edge information and an edge-aware dissimilarity is defined. Then, a new merging method termed as one iteration merging is proposed, which leverages the continuity of the adjacent pixels and ensures the connectivity of superpixel. Furthermore, instead of iterative clustering, the one iteration merging is achieved in only one iteration without determining the number of iterations and hence efficient in computation. Experiments on two real SAR images demonstrate that the proposed method yields substantially better performance than some state-of-the-art methods.
机译：大多数现有的超像素生成方法是根据当地迭代集群。然而，这种方法具有以下缺点：1）这些方法需要多次迭代和迭代的次数是难以确定和2）将所生成的超像素缺乏明确的连接而无需后处理步骤。旨在克服的局限性，我们提出了一个迭代合并（ESOM）的合成孔径雷达（SAR）图像边缘感知中超像素的生成。在具体的，我们介绍利用高斯形的窗口来提取边缘信息和边缘感知相异一个基于比率的边缘检测器的定义。然后，新的合并方法称为提出一个迭代合并，这利用了相邻像素的连续性，并确保超像素的连通性。此外，代替迭代聚类，所述一个迭代合并仅在一个迭代中，而无需确定的迭代次数和计算因此高效实现。在两个真实SAR图像实验证明比状态的最先进的一些方法中所提出的方法的产率基本上更好的性能。

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