Fuzzy Superpixels for Polarimetric SAR Images Classification
Fuzzy Superpixels for Polarimetric SAR Images Classification
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DOI：
10.1109/tfuzz.2018.2814591
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发表时间：
2018-03
影响因子：
11.9
通讯作者：
Yuwei Guo;L. Jiao;Shuang Wang;Shuo Wang;Fang Liu;Wenqiang Hua
中科院分区：
文献类型：
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作者：
Yuwei Guo;L. Jiao;Shuang Wang;Shuo Wang;Fang Liu;Wenqiang Hua
超像素技术在计算机视觉应用中受到广泛关注。每种超像素算法都有其自身的优势。为特定应用选择更合适的超像素算法可以提高应用的性能。近年来，超像素被广泛应用于极化合成孔径雷达（PolSAR）图像分类。然而，没有专门为图像分类设计的超像素算法。据信，混合超像素和纯超像素都存在于图像中。然而，混合超像素对分类精度有负面影响。因此，有必要生成包含尽可能少的混合超像素的超像素用于图像分类。在本文中，首先，一个新的超像素的概念，命名为模糊超像素，提出了减少混合超像素的产生。在模糊超像素中，并非所有像素都被分配给对应的超像素。我们宁愿忽略这些像素，也不愿将它们分配给不合适的超像素。其次，提出了一种新的模糊超像素生成算法FuzzyS（FS），用于极化SAR图像分类。利用三幅极化SAR图像验证了FS算法的有效性。实验结果表明，所提出的FS算法优于几个国家的最先进的超像素算法。
Superpixels technique has drawn much attention in computer vision applications. Each superpixels algorithm has its own advantages. Selecting a more appropriate superpixels algorithm for a specific application can improve the performance of the application. In the last few years, superpixels are widely used in polarimetric synthetic aperture radar (PolSAR) image classification. However, no superpixel algorithm is especially designed for image classification. It is believed that both mixed superpixels and pure superpixels exist in an image. Nevertheless, mixed superpixels have negative effects on classification accuracy. Thus, it is necessary to generate superpixels containing as few mixed superpixels as possible for image classification. In this paper, first, a novel superpixels concept, named fuzzy superpixels, is proposed for reducing the generation of mixed superpixels. In fuzzy superpixels, not all pixels are assigned to a corresponding superpixel. We would rather ignore the pixels than assigning them to improper superpixels. Second, a new algorithm, named FuzzyS (FS), is proposed to generate fuzzy superpixels for PolSAR image classification. Three PolSAR images are used to verify the effect of the proposed FS algorithm. Experimental results demonstrate the superiority of the proposed FS algorithm over several state-of-the-art superpixels algorithms.