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首页> 外文期刊>International journal of image and data fusion >Unsupervised classification of polarimetric synthetic aperture radar images using kernel fuzzy C-means clustering
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Unsupervised classification of polarimetric synthetic aperture radar images using kernel fuzzy C-means clustering

机译：基于核模糊C均值聚类的极化合成孔径雷达图像的无监督分类

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In this article, an unsupervised clustering approach, known as the kernel fuzzy C-means (KFCM), which is based on Cloude's decomposition, is used for polarimetric synthetic aperture radar (POLSAR) image classification. The KFCM algorithm is an improved version of the fuzzy C-means (FCM) algorithm. It replaces the original Euclidean distance measure by the kernel-induced distance. The original sample space, coherency matrix T, is mapped to a higher-dimensional feature space so as to simplify the complex POLSAR data by using the Gaussian kernel function. The method has three main steps: first, the eight initial centres are obtained by averaging the coherency matrix within each partition according to the classical H/α plane so as to preserve the polarimetric property reasonably well; second, the related parameters of the KFCM algorithm are iteratively refined and third, the membership matrix is defuzzified by using the maximum membership decision rule. The distance measure used in the algorithm is derived from the complex Wishart distribution of the pixel data presented in the coherence data. The KFCM method not only takes advantage of the polarimetric scattering properties but also utilises the kernel method to cluster the nonlinear structure data with noise. The feasibility of this approach was tested by using two JPL/AIRSAR polarimetric SAR images. The experimental results showed that the KFCM clustering algorithm was better than the FCM clustering algorithm at classifying the POLSAR images.
机译：在本文中，基于Cloude分解的一种称为核模糊C均值（KFCM）的无监督聚类方法被用于极化合成孔径雷达（POLSAR）图像分类。 KFCM算法是模糊C均值（FCM）算法的改进版本。它用内核引起的距离替换了原始的欧几里得距离度量。将原始样本空间（相干矩阵T）映射到更高维的特征空间，以便使用高斯核函数简化复杂的POLSAR数据。该方法包括三个主要步骤：首先，根据经典H /α平面对每个分区内的相干矩阵求平均，从而获得八个初始中心，从而合理地保留偏振特性。其次，对KFCM算法的相关参数进行迭代细化；其次，使用最大隶属度决策规则对隶属度矩阵进行模糊化处理。该算法中使用的距离度量是从相干数据中呈现的像素数据的复杂Wishart分布得出的。 KFCM方法不仅利用了极化散射特性，而且利用核方法将具有噪声的非线性结构数据聚类。通过使用两个JPL / AIRSAR极化SAR图像测试了此方法的可行性。实验结果表明，在对SARSAR图像进行分类时，KFCM聚类算法优于FCM聚类算法。

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