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Polarimetric synthetic aperture radar image classification using fuzzy logic in the H/alpha-Wishart algorithm

机译:H / alpha-Wishart算法中使用模糊逻辑的极化合成孔径雷达图像分类

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To solve the problem that the H/alpha-Wishart unsupervised classification algorithm can generate only inflexible clusters due to arbitrarily fixed zone boundaries in the clustering processing, a refined fuzzy logic based classification scheme called the H/alpha-Wishart fuzzy clustering algorithm is proposed in this paper. A fuzzy membership function was developed for the degree of pixels belonging to each class instead of an arbitrary boundary. To devise a unified fuzzy function, a normalized Wishart distance is proposed during the clustering step in the new algorithm. Then the degree of membership is computed to implement fuzzy clustering. After an iterative procedure, the algorithm yields a classification result. The new classification scheme is applied to two L-band polarimetric synthetic aperture radar (PolSAR) images and an X-band high-resolution PolSAR image of a field in LingShui, Hainan Province, China. Experimental results show that the classification precision of the refined algorithm is greater than that of the H/alpha-Wishart algorithm and that the refined algorithm performs well in differentiating shadows and water areas. (C) The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License.
机译:针对H / alpha-Wishart模糊聚类算法在聚类过程中由于区域边界任意固定而只能生成非柔性聚类的问题,提出了一种改进的基于模糊逻辑的分类方案,称为H / alpha-Wishart模糊聚类算法。这篇报告。针对属于每个类别的像素的程度而不是任意边界开发了模糊隶属度函数。为了设计统一的模糊函数,在新算法的聚类步骤中提出了归一化的Wishart距离。然后计算隶属度以实现模糊聚类。经过一个迭代过程,该算法得出分类结果。新的分类方案被应用于中国海南省陵水市的两个L波段极化合成孔径雷达(PolSAR)图像和X波段高分辨率PolSAR图像。实验结果表明,改进算法的分类精度高于H / alpha-Wishart算法,并且在区分阴影和水域方面表现良好。 (C)作者。由SPIE根据Creative Commons Attribution 3.0 Unported License发布。

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