| 基于改进的模糊c-均值聚类方法遥感影像分类研究(英文) |
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| 作者单位： | School of Remote Sensing and Information Engineering Wuhan University，129 Luoya Road，Wuhan 430079，China |
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| 基金项目： | Supported by the Scientific Research Foundation for the Returned Overseas Chinese Scholars, State Education Ministry. |
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| 摘 要： | Classification is always the key point in the field of remote sensing. Fuzzy c-Means is a traditional clustering algorithm that has been widely used in fuzzy clustering. However, this algorithm usually has some weaknesses, such as the problems of falling into a local minimum, and it needs much time to accomplish the classification for a large number of data. In order to overcome these shortcomings and increase the classification accuracy, Gustafson-Kessel （GK） and Gath-Geva （GG） algorithms are proposed to improve the traditional FCM algorithm which adopts Euclidean distance norm in this paper. The experimental result shows that these two methods are able to detect clusters of varying shapes, sizes and densities which FCM cannot do. Moreover, they can improve the classification accuracy of remote sensing images.
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| 关 键 词： | 模糊数据 测绘技术 遥控技术 空间科学 |
Remote sensing image classification based on improved fuzzy <Emphasis Type="Italic">c</Emphasis>-means |
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| Authors: | Jie Yu Peihuang Guo Pinxiang Chen Zhongshan Zhang Wenbin Ruan |
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| Institution: | (1) School of Remote Sensing and Information Engineering, Wuhan University, 129 Luoya Road, Wuhan, 430079, China |
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| Abstract: | Classification is always the key point in the field of remote sensing. Fuzzy c-Means is a traditional clustering algorithm that has been widely used in fuzzy clustering. However, this algorithm usually has some weaknesses, such as the problems of falling into a local minimum, and it needs much time to accomplish the classification for a large number of data. In order to overcome these shortcomings and increase the classifi-cation accuracy, Gustafson-Kessel (GK) and Gath-Geva (GG) algorithms are proposed to improve the tradi-tional FCM algorithm which adopts Euclidean distance norm in this paper. The experimental result shows that these two methods are able to detect clusters of varying shapes, sizes and densities which FCM cannot do. Moreover, they can improve the classification accuracy of remote sensing images. |
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| Keywords: | FCM algorithm GK algorithm GG algorithm remote sensing classification |
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