Soft partition of FCM clustering results: A case study on the clustering of urban underlying surface from remotely sensed imagery

ZHOU Zi-min; ZHOU Jian-hua

doi:10.3969/j.issn.1000-5641.2016.04.017

Issue 4

Sep. 2016

Turn off MathJax

Article Contents

Article Navigation > Journal of East China Normal University (Natural Sciences) > 2016 > (4): 150-157

ZHOU Zi-min, ZHOU Jian-hua. Soft partition of FCM clustering results: A case study on the clustering of urban underlying surface from remotely sensed imagery[J]. Journal of East China Normal University (Natural Sciences), 2016, (4): 150-157. doi: 10.3969/j.issn.1000-5641.2016.04.017

Citation:

ZHOU Zi-min, ZHOU Jian-hua. Soft partition of FCM clustering results: A case study on the clustering of urban underlying surface from remotely sensed imagery[J]. Journal of East China Normal University (Natural Sciences), 2016, (4): 150-157. doi: 10.3969/j.issn.1000-5641.2016.04.017

Citation:

PDF( 3075 KB)

Soft partition of FCM clustering results: A case study on the clustering of urban underlying surface from remotely sensed imagery

doi: 10.3969/j.issn.1000-5641.2016.04.017

ZHOU Zi-min¹,
ZHOU Jian-hua^{2
,
,}

1.
School of Geographic Sciences, East China Normal University, Shanghai 200241, China;
2.
Key Lab of Geographical Information Science, Ministry of Education, East China Normal University, Shanghai 200241, China.

Received Date: 2015-06-17
Publish Date: 2016-07-25

Abstract

Abstract

FCM is one of the most widely used fuzzy clustering methods. Being different from the distinct clustering, the fuzzy clustering provides variations of membership of entity. The variations serve as useful references for adjusting centroids and allocating clusters during and after the clustering respectively. It is a commonly used way in FCM applications to allocate a pixel according to the maximum of memberships of this pixel owns. Such hard partition will likely allocate the pixel to an inappropriate class. Therefore, a soft partition approach, called as SPFCM, has been investigated in this paper. The soft partition depends on both the dispersion degree of the memberships (represented by the standard deviation between the memberships) and the spatial dependence of pixels (indicated by the density of neighborhood pixels). There are four steps to conduct the soft partition：1) Get a membership matrix by FCM clustering. 2) Calculate the standard deviation of class membership for each pixel from the membership matrix. 3) Compute the density of neighboring elements for each class in the pixels neighbor and these elements are weighted by their membership. 4) Take the results from step 2 and 3 as references to allocate the centre pixel. To release from manual operation, some important adjustable parameters (e.g. neighborhood window size, etc.) are determined by adaptive calculation. Experiments indicate that the average area of clustering patches can be applied to derive the base number of window size for calculating the density of neighboring elements. MATLAB simulation tests show that the accuracy of allocation by SPFCM is 9% higher than that by the hard one involved with the maximum membership.
- FCM clustering defuzzifying dispersion degree of membership spatial dependency,

FullText(HTML)

References(1)

References

[1]

[1] ZADEH L A. Fuzzy sets[J]. Information and control, 1965, 8(3): 338-353.
[2] RUSPINI E H. A new approach to clustering[J]. Information and control, 1969, 15(1): 22-32.
[3] DUNN J C. A Fuzzy relative of the ISODATA process and its use in detecting compact well-separated clusters[J]. Cybernetics, 1973, 3(3): 32-57.
[4] BEZDEK J C. Pattern Recognition with Fuzzy Objective Function Algorithms[M]. New York: Plenum Press, 1981.
[5] YAGER R R, FILEV D P. Approximate clustering via the mountain method[J]. Systems, Man and Cybernetics, IEEE Transactions on, 1994, 24(8): 1279-1284.
[6] 张爱华. 基于模糊聚类分析的图像分割技术研究[D].武汉:华中科技大学, 2004. DOI:10.7666/d.d002381.
[7] 王磊, 王伟, 李玉祥. 基于人工免疫细胞模型的模糊聚类算法[J]. 计算机工程, 2011, 37(5): 13-15.
[8] GHAFFARIAN S, GHAFFARIAN S. Automatic histogrambased fuzzy C-means clustering for remote sensing imagery[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2014, 97: 46-57.
[9] BEZDEK J C. A Convergence Theorem for the Fuzzy ISODATA Clustering Algorithms[J]. IEEE transactions on pattern analysis and machine intelligence, 1980, 2(1): 1-8.
[10] 张运杰. 基于模糊系统理论的图像分割技术研究[D]. 大连: 大连海事大学, 2007.
[11] FALASCONI M, GUTIERREZ A, PARDO M, et al. A stability based validity method for fuzzy clustering[J]. Pattern Recognition, 2010, 43(4): 1292-1305.
[12] ALIK K R. Cluster validity index for estimation of fuzzy clusters of different sizes and densities[J]. Pattern Recognition, 2010, 43(10): 3374-3390.
[13] 郑宏亮,徐本强,赵晓慧,等. 新的模糊聚类有效性指标[J]. 计算机应用, 2014, 34(8): 2166-2169.
[14] LIEW A W C, LEUNG S H, LAU W H. Fuzzy image clustering incorporating spatial continuity[J]. IEE Proceedings-Vision, Image and Signal Processing, 2000, 147(2): 185-192.
[15] 葛宏立. 面向类的图像分割方法研究[D]. 北京: 北京林业大学, 2004.
[16] 依玉峰, 高立群, 郭丽. 基于相似类合并 FCM 在图像分割中的应用[J]. 东北大学学报（自然科学版）, 2012, 33(7): 930-933.
[17] 张文君. 基于特征库和模糊技术的遥感图像分类研究[D]. 成都: 电子科技大学, 2007.
[18] 周绍光, 贾凯华, 殷楠. 一种利用像素邻域信息的模糊聚类图像分割算法[J]. 测绘科学, 2013, 38(001): 153-155. 
[19] PIMENTEL B A, DE SOUZA R M. A multivariate fuzzy c-means method[J]. Applied Soft Computing, 2013, 13(4): 1592-1607.
[20] PIMENTEL B A, DE SOUZA R M. A weighted multivariate Fuzzy C-Means method in interval-valued scientific production data[J]. Expert Systems with Applications, 2014, 41(7): 3223-3236.
[21] BEZDEK J C, HATHAWAY R J. Optimization of fuzzy clustering criteria using genetic algorithms[C]//Evolutionary Computation, 1994. IEEE World Congress on Computational Intelligence, Proceedings of the First IEEE Conference on. IEEE, 1994: 589-594.
[22] BROUWER R K, GROENWOLD A. Modified fuzzy c-means for ordinal valued attributes with particle swarm for optimization[J]. Fuzzy sets and systems, 2010, 161(13): 1774-1789.
[23] 赵小强, 刘悦婷. 基于选择和变异机制的蛙跳 FCM 算法[J]. 计算机应用研究, 2012, 29(6): 2068-2071.
[24] 李德毅, 孟海军. 隶属云和隶属云发生器[J]. 计算机研究与发展, 1995, 32(6): 15-20.
[25] 邸凯昌, 李德毅. 云理论及其在空间数据发掘和知识发展中的应用[J]. 中国图象图形学报: A 辑, 1999, 4(11): 930-935.
[26] PRICE J C. Estimating vegetation amount from visible and near infrared reflectances[J]. Remote Sensing of Environment, 1992, 41(1): 29-34.
[27] ZHOU J, QIN J, GAO K, et al. Featurelocation analyses for identification of urban tree species from very high resolution remote sensing data[J]. Ecological Informatics, 2015, 29:16-24. 
[28] GAO B C. NDWI—a normalized difference water index for remote sensing of vegetation liquid water from space[J]. Remote sensing of environment, 1996, 58(3): 257-266.
[29] 查勇, 倪绍祥, 杨山. 一种利用TM图像自动提取城镇用地信息的有效方法[J]. 遥感学报, 2003, 7(1): 37-40.
[30] OHTSU N. A Threshold Selection Method from Gray-Level Histograms[J]. Systems Man & Cybernetics IEEE Transactions on, 1979, 9(1):62-66. 
[31] MEYER G E, NETO J C. Verification of color vegetation indices for automated crop imaging applications[J]. Computers and Electronics in Agriculture, 2008, 63(2): 282-293.
[32] 周坚华, 周一凡, 穆望舒. 城镇绿地树种识别的数学描述符[J]. 遥感学报, 2011, 15(3): 524-538.