A New Weighted Fuzzy C-Means Clustering Algorithm for Remotely Sensed Image Classification
A New Weighted Fuzzy C-Means Clustering Algorithm for Remotely Sensed Image Classification
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DOI：
10.1109/jstsp.2010.2096797
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发表时间：
2011-06-01
影响因子：
7.5
通讯作者：
Kuo, Bor-Chen
中科院分区：
文献类型：
--
作者：
Hung, Chih-Cheng;Kulkarni, Sameer;Kuo, Bor-Chen
模糊聚类模型是模式和图像分类中对给定的数据集进行聚类分析的重要工具。本文提出了一种新的加权模糊C-均值（NW-FCM）算法，以提高模糊C-均值（FCM）和模糊加权C-均值（FWCM）模型在高维多类模式识别问题中的性能。在NW-FCM中使用的方法是来自非参数加权特征提取（NWFE）的加权平均值和来自判别分析特征提取（DAFE）的聚类平均值的概念。这两个概念结合在NW-FCM的无监督聚类。与FCM相比，NW-FCM的主要特点是包含加权平均值以提高准确性，并且与FWCM相比，包含每个聚类的质心以提高稳定性。本文的目的是改进著名的模糊C均值算法（FCM）和最近提出的模糊加权C均值算法（FWCM）。我们的发现是，该算法给出了更高的分类精度和稳定性比FCM和FWCM。对合成数据和真实的数据的实验结果表明，该算法的聚类效果优于FCM和FWCM算法，特别是对高光谱图像。
Fuzzy clustering model is an essential tool to find the proper cluster structure of given data sets in pattern and image classification. In this paper, a new weighted fuzzy C-Means (NW-FCM) algorithm is proposed to improve the performance of both FCM and FWCM models for high-dimensional multiclass pattern recognition problems. The methodology used in NW-FCM is the concept of weighted mean from the nonparametric weighted feature extraction (NWFE) and cluster mean from discriminant analysis feature extraction (DAFE). These two concepts are combined in NW-FCM for unsupervised clustering. The main features of NW-FCM, when compared to FCM, are the inclusion of the weighted mean to increase the accuracy, and, when compared to FWCM, the centroid of each cluster is included to increase the stability. The motivation of this work is to meliorate the well-known fuzzy C-Means algorithm (FCM) and a recently proposed fuzzy weighted C-Means algorithm (FWCM). Our finding is that the proposed algorithm gives greater classification accuracy and stability than that of FCM and FWCM. Experimental results on both synthetic and real data demonstrate that the proposed clustering algorithm will generate better clustering results than those of FCM and FWCM algorithms, in particularly for hyperspectral images.