Multispectral Remote Sensing Image Segmentation Based on FCM Algorithm with Unknown Number of Clusters

ZHAO Quanhua; LIU Xiaoyan; ZHAO Xuemei; LI Yu

doi:10.11999/JEIT170397

Volume 40 Issue 1

Jan. 2018

Turn off MathJax

Article Contents

Article Navigation > Journal of Electronics & Information Technology > 2018 > 40(1): 157-165

ZHAO Quanhua, LIU Xiaoyan, ZHAO Xuemei, LI Yu. Multispectral Remote Sensing Image Segmentation Based on FCM Algorithm with Unknown Number of Clusters[J]. Journal of Electronics & Information Technology, 2018, 40(1): 157-165. doi: 10.11999/JEIT170397

Citation:

ZHAO Quanhua, LIU Xiaoyan, ZHAO Xuemei, LI Yu. Multispectral Remote Sensing Image Segmentation Based on FCM Algorithm with Unknown Number of Clusters[J]. Journal of Electronics & Information Technology, 2018, 40(1): 157-165. doi: 10.11999/JEIT170397

Citation:

PDF( 18495 KB)

Multispectral Remote Sensing Image Segmentation Based on FCM Algorithm with Unknown Number of Clusters

doi: 10.11999/JEIT170397

Received Date: 2017-05-02
Rev Recd Date: 2017-09-20
Publish Date: 2018-01-19

Abstract

Abstract

In order to automatically determine the number of clusters in multispectral remote sensing image segmentation, Fuzzy C-Means (FCM) algorithm with unknown number of clusters is proposed. First of all, a new dissimilarity measure between a pixel and a cluster is defined. The fuzzy membership function and cluster center are obtained through minimizing the objective function. Then, the relationship between fuzzy factor and the number of clusters is studied. The optimal fuzzy factor is selected by defining the Partition Entropy (PE) index and corresponding to the minimum of fuzzy factor after the convergence of PE values. According to the relationship between the fuzzy factor and the number of clusters, the optimal number of clusters is obtained, and the variable cluster segmentation of the image is realized. The analysis based on segmentation results of synthesized image and real multispectral remote sensing images show that the proposed algorithm can automatically determine the number of clusters and obtain the ideal segmentation results simultaneously. It provides a new method for automatically determine the number of clusters of remote sensing image.
- Multispectral remote sensing image,
- Fuzzy membership degree,
- Fuzzy factor,
- Segmentation with unknown number of clusters,
- Partition Entropy (PE) index

FullText(HTML)

References(32)

References

CAO Jiazi and SONG Aiguo. Research on the texture image segmentation method based on Markov random field[J]. Chinese Journal of Scientific Instrument, 2015, 36(4): 776-786.

曹家梓, 宋爱国. 基于马尔科夫随机场的纹理图像分割方法研究[J]. 仪器仪表学报, 2015, 36(4): 776-786.

李旭超, 朱善安. 影像分割中的马尔可夫随机场方法综述[J]. 中国图象图形学报, 2007, 12(5): 789-797.

LI Xuchao and ZHU Shanan. A survey of the Markov random field method for image segmentation[J]. Journal of Image and Graphics, 2007, 12(5): 789-797.

郑玮, 康戈文, 陈武凡, 等. 基于马尔可夫随机场的无监督遥感图像分割算法[J]. 遥感学报, 2008, 12(2): 246-252.

ZHENG Wei, KANG Gewen, CHEN Wufan, et al. Unsupervised segmentation of remote sensing images based on Fuzzy Markov Random Field model[J]. Journal of Remote Sensing, 2008, 12(2): 246-252.

巫兆聪, 胡忠文, 张谦. 结合光谱、纹理与形状结构信息的遥感影像分割方法[J]. 测绘学报, 2013, 42(1): 44-50.

WU Zhaocong, HU Zhongwen, and ZHANG Qian. On combining spectral, textural and shape features for remote sensing image segmentation[J]. Acta Geodaetica et Cartographica Sinica, 2013, 42(1): 44-50.

陈海鹏, 申铉京, 龙建武, 等. 自动确定聚类个数的模糊聚类算法[J]. 电子学报, 2017, 45(3): 687-694. doi: 10.3969/j.issn. 0372-2112. 2017.03.028.

CHEN Haipeng, SHEN Xuanjing, LONG Jianwu, et al. Fuzzy clustering algorithm for automatic identification of clusters[J]. Acta Electronica Sinica, 2017, 45(3): 687-694. doi: 10.3969/ j.issn.0372-2112.2017.03.028.

肖满生, 肖哲, 文志诚, 等. 一种空间相关性与隶属度平滑的FCM改进算法[J]. 电子与信息学报, 2017, 39(5): 1123-1129. doi: 10.11999/JEIT160710.

XIAO Mansheng, XIAO Zhe, WEN Zhicheng, et al. Improved FCM clustering algorithm based on spatial correlation and membership smoothing[J]. Journal of Electronics Information Technology, 2017, 39(5): 1123-1129. doi: 10.11999/JEIT160710.

周文刚, 孙挺, 朱海. 一种基于自适应空间信息改进FCM的图像分割算法[J]. 计算机应用研究, 2015, 32(7): 2205-2208. doi: 10.3969/j.issn.1001-3695.2015.07.070.

ZHOU Wengang, SUN Ting, and ZHU Hai. Image segmentation algorithm based on FCM optimized by adaptive spatial information[J]. Application Research of Computers, 2015, 32(7): 2205-2208. doi: 10.3969/j.issn.1001- 3695.2015.07.070.

赵雪梅, 李玉, 赵泉华. 结合高斯回归模型和隐马尔可夫随机场的模糊聚类图像分割[J]. 电子与信息学报, 2014, 36(11): 2730-2736. doi: 10.3724/SP.J.1146.2013.01751.

ZHAO Xuemei, LI Yu, and ZHAO Quanhua. Image segmentation by fuzzy clustering algorithm combining hidden Markov Random Field and Gaussian Regression model[J]. Journal of Electronics Information Technology, 2014, 36(11): 2730-2736. doi: 10.3724/SP.J.1146.2013.01751.

沈照庆, 舒宁, 龚衍, 等. 基于改进模糊ISODATA算法的遥感影像非监督聚类研究[J]. 遥感信息, 2008(5): 28-32.

SHEN Zhaoqing, SHU Ning, GONG Yan, et al. Study on the supervised classification of remote sensing image based on a modified fuzzy-ISODATA algorithm[J]. Remote Sensing Information, 2008(5): 28-32.

王建英, 孙德山, 张永. 基于马氏距离的FCM图像分割算法[J]. 计算机工程与应用, 2010, 46(1): 147-149. doi: 10.3778/ j.issn.1002-8331.2010.01.045.

WANG Jianying, SUN Deshan, and ZHANG Yong. Mahalanobis distance-based FCM image segmentation algorithm[J]. Computer Engineering and Applications, 2010, 46(1): 147-149. doi: 10.3778/j.issn.1002-8331.2010.01.045.

BEZDEK J C. Patten Recognition with Fuzzy Objective Function Algorithms[M]. New York: Plenum Press, 1981: 79-88.

PAL N R and BEZDEK J C. On cluster validity for the fuzzy c-means model[J]. IEEE Transactions on Fuzzy System, 1995, 3(3): 370-379.

宫改云, 高新波, 伍忠东. FCM聚类算法中模糊加权指数m的优选方法[J]. 模糊系统与数学, 2005, 19(1): 143-148.

GONG Gaiyun, GAO Xinbo, and WU Zhongdong. An optimal choice method of parameter m in FCM clustering algorithm[J]. Fuzzy Systems and Mathematics, 2005, 19(1): 143-148.

肖满生, 张居武. 一种基于子集测度的FCM聚类加权指数计算方法[J]. 模糊系统与数学, 2013, 27(2): 136-141. doi: 10.3969/j.issn.1001-7402.2013.02.022.

XIAO Mansheng and ZHANG Juwu. The weighted exponent calculation method of FCM clustering based on subset measuring[J]. Fuzzy Systems and Mathematics, 2013, 27(2): 136-141. doi: 10.3969/j.issn.1001-7402.2013.02.022.

刘永利, 付丽丽. FCCM 算法中基于划分熵的参数优选方法[J]. 河南理工大学学报, 2016, 35(2): 248-253. doi: 10.16186/ j.cnki.2016.02.019.

LIU Yongli and FU Lili. A method of parameter optimization based on partition entropy in FCCM algorithm[J]. Journal of Henan Polytechnic University, 2016, 35(2): 248-253. doi: 10.16186/j.cnki.2016.02.019.

赵雪梅, 李玉, 赵泉华. 参数自适应的可变类FLICM灰度图像分割算法[J]. 控制与决策, 2017, 32(2): 262-268. doi: 10.13195/ j.kzyjc.2016.0050.

ZHAO Xuemei, LI Yu, and ZHAO Quanhua. Self-adaptive FLICM algorithm for gray image segmentation with unknown number of clusters[J]. Control and Decision, 2017, 32(2): 262-268. doi: 10.13195/j.kzyjc.2016.0050.

王玉, 李玉, 赵泉华. 可变类多光谱遥感图像分割[J]. 遥感学报, 2016, 20(6): 1381-1390. doi: 10.11834/jrs.20165076.

WANG Yu, LI Yu, and ZHAO Quanhua. Integration of multispectral remote sensing image segmentation with unknown number of classes[J]. Journal of Remote Sensing, 2016, 20(6): 1381-1390. doi: 10.11834/jrs.20165076.

Relative Articles

Supplements(0)

Cited By

Proportional views