| 基于参数优化改进型可能聚类的遥感图像分割 |
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| 引用本文： | 武斌,黄庆丰,魏元春.基于参数优化改进型可能聚类的遥感图像分割[J].计算机与农业,2011(12):31-34. |
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| 作者姓名： | 武斌 黄庆丰 魏元春 |
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| 作者单位： | [1]安徽农业大学信息与计算机学院,安徽合肥230036 [2]安徽农业大学林学与园林学院,安徽合肥230036 |
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| 摘 要： | 可能聚类算法（PCA）和可能C-均值聚类算法（PCM）对初始值非常敏感,容易产生一致性聚类。改进型可能C-均值聚类算法（IPCM）能解决PCM的问题,然而IPCM的执行更依赖于参数。IPCM必须计算参数两次,因此聚类时间长。为了克服PCA和IPCM的缺点,进而应用于复杂的遥感图像分割,将PCA和IPCM相结合,提出了一种基于参数优化的改进型可能聚类算法（IPCAOP）。实验表明,IPCAOP在处理遥感图像分割方面明显优于模糊C-均值聚类（FCM）和IPCM。
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| 关 键 词： | 模糊C-均值聚类 可能性模糊C-均值聚类 改进的可能性模糊C-均值聚类 遥感图像 |
Remote Sensing Image Segmentation Using Improved Possibilistic Clustering Algorithm with Optimized Parameters |
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| WU Bin,HUANG Qingfeng,WEI Yuanchun.Remote Sensing Image Segmentation Using Improved Possibilistic Clustering Algorithm with Optimized Parameters[J].Computer and Agriculture,2011(12):31-34. |
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| Authors: | WU Bin HUANG Qingfeng WEI Yuanchun |
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| Institution: | 1.College of Information and Computer Science Anhui Agricultural University,Anhui Hefei 230036; 2.College of Forestry and Landscape Architecture Anhui Agricultural University,Anhui Hefei 230036) |
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| Abstract: | Possibilistic clustering algorithm（PCA） and possibilistic c-means clustering（PCM） were very sensitive to initialization value which had undesirable tendency to produce coincident clusters.An improved possibilistic c-means（IPCM） algorithm was proposed to solve the problems of PCM.However,the performance of IPCM depended heavily on the parameters.IPCM must compute the parameters twice,so it was time-consuming.In order to solve the problems of PCA and IPCM,an improved possibilistic clustering algorithm with optimized parameters（IPCAOP） was proposed to combine PCA and IPCM.Our experimental results showed that compared with fuzzy c-means clustering（FCM） and IPCM,the proposed algorithm was better in remote sensing image segmentation. |
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| Keywords: | fuzzy c-means possibilistic c-means improved possibilistic c-means remote sensing image |
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