条件随机场模型约束下的遥感影像模糊C-均值聚类算法

doi:10.11947/j.AGCS.2016.20150621

测绘学报 ›› 2016, Vol. 45 ›› Issue (12): 1441-1447.doi: 10.11947/j.AGCS.2016.20150621

条件随机场模型约束下的遥感影像模糊C-均值聚类算法

王少宇¹, 焦洪赞², 钟燕飞³

1. 武汉大学遥感信息工程学院, 湖北武汉 430079;
2. 武汉大学城市设计学院, 湖北武汉 430072;
3. 武汉大学测绘遥感信息工程国家重点实验室, 湖北武汉 430079

收稿日期:2015-12-11 修回日期:2016-09-09 出版日期:2016-12-20 发布日期:2017-01-02
通讯作者: 焦洪赞 E-mail:jiaohongzan@whu.edu.cn
作者简介:王少宇(1995-),男,硕士生,研究方向为遥感图像模式识别。E-mail:240020340@qq.com
基金资助:
国家自然科学基金（41401400；51278385）

A Modified FCM Classifier Constrained by Conditional Random Field Model for Remote Sensing Imagery

WANG Shaoyu¹, JIAO Hongzan², ZHONG Yanfei³

1. School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China;
2. School of Urban Design, Wuhan University, Wuhan 430072, China;
3. State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China

Received:2015-12-11 Revised:2016-09-09 Online:2016-12-20 Published:2017-01-02
Supported by:
The National Natural Science Foundation of China (Nos.41401400,51278385)

摘要/Abstract

摘要： 遥感影像具有丰富的空间相关信息，而传统的基于像元光谱的聚类算法并不能将空间信息融入聚类，聚类结果往往不好。针对这一问题，本文提出了一种条件随机场模型约束下的模糊C-均值聚类算法，通过邻域像元的分类先验信息对中心像元的类别进行约束从而提取空间相关信息，基于二阶条件随机场将光谱信息和空间相关信息同时融入聚类，并使用环形置信度迭代算法得到像元分类后验概率的全局最优推测。试验证明，本文算法能够有效地保持地物的形状特征，分类精度相比传统算法有所提高。

关键词: 遥感影像聚类, 模糊C-均值, 条件随机场, 空间相关信息

Abstract: Remote sensing imagery has abundant spatial correlation information, but traditional pixel-based clustering algorithms don't take the spatial information into account, therefore the results are often not good. To this issue, a modified FCM classifier constrained by conditional random field model is proposed. Adjacent pixels' priori classified information will have a constraint on the classification of the center pixel, thus extracting spatial correlation information. Spectral information and spatial correlation information are considered at the same time when clustering based on second order conditional random field. What's more, the global optimal inference of pixel's classified posterior probability can be get using loopy belief propagation. The experiment shows that the proposed algorithm can effectively maintain the shape feature of the object, and the classification accuracy is higher than traditional algorithms.

Key words: clustering for remote sensing imagery, fuzzy C-means, conditional random field, spatial correlation information

中图分类号:

P237

王少宇, 焦洪赞, 钟燕飞. 条件随机场模型约束下的遥感影像模糊C-均值聚类算法[J]. 测绘学报, 2016, 45(12): 1441-1447.

WANG Shaoyu, JIAO Hongzan, ZHONG Yanfei. A Modified FCM Classifier Constrained by Conditional Random Field Model for Remote Sensing Imagery[J]. Acta Geodaetica et Cartographica Sinica, 2016, 45(12): 1441-1447.

参考文献

[1] TAŞDEMIR K, MERÉNYI E. A Validity Index for Prototype-based Clustering of Data Sets with Complex Cluster Structures[J]. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 2011, 41(4):1039-1053.
[2] JAIN A K. Data Clustering:50 Years beyond K-means[J]. Pattern Recognition Letters, 2010, 31(8):651-666.
[3] MAULIK U, SAHA I. Modified Differential Evolution Based Fuzzy Clustering for Pixel Classification in Remote Sensing Imagery[J]. Pattern Recognition, 2009, 42(9):2135-2149.
[4] LLOYD S. Least Squares Quantization in PCM[J]. IEEE Transactions on Information Theory, 1982, 28(2):129-137.
[5] BALL G H, HALL D J. ISODATA, A Novel Method of Data Analysis and Pattern Classification[R]. Technical Report AD0699616. Menlo Park, CA:Stanford Research Institute, 1965.
[6] 李雷, 罗红旗, 丁亚丽. 自适应约束模糊C均值聚类算法[J]. 模糊系统与数学, 2010, 24(5):126-130. LI Lei, LUO Hongqi, DING Yali. Adaptive Constrained FCM Clustering Algorithm[J]. Fuzzy Systems and Mathematics, 2010, 24(5):126-130.
[7] 耿连英, 贺兴时, 贺飞跃. 基于模糊分类的MRF图像恢复方法[J]. 计算机工程, 2011, 37(10):198-199, 221. GENG Lianying, HE Xingshi, HE Feiyue. MRF Image Restoration Method Based on Fuzzy Classification[J]. Computer Engineering, 2011, 37(10):198-199, 221.
[8] ZHONG Yanfei, MA Ailong, ZHANG Liangpei. An Adaptive Memetic Fuzzy Clustering Algorithm with Spatial Information for Remote Sensing Imagery[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2014, 7(4):1235-1248.
[9] BANDYOPADHYAY S, MAULIK U, MUKHOPADHYAY A. Multiobjective Genetic Clustering for Pixel Classification in Remote Sensing Imagery[J]. IEEE Transactions on Geoscience and Remote Sensing, 2007, 45(5):1506-1511.
[10] GHOSH A, MISHRA N S, GHOSH S. Fuzzy Clustering Algorithms for Unsupervised Change Detection in Remote Sensing Images[J]. Information Sciences, 2011, 181(4):699-715.
[11] NIAZMARDI S, HOMAYOUNI S, SAFARI A. An Improved FCM Algorithm Based on the SVDD for Unsupervised Hyperspectral Data Classification[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2013, 6(2):831-839.
[12] AHMED M N, YAMANY S M, MOHAMED N, et al. A Modified Fuzzy C-means Algorithm for Bias Field Estimation and Segmentation of MRI Data[J]. IEEE Transactions on Medical Imaging, 2002, 21(3):193-199.
[13] 刘启亮, 邓敏, 石岩, 等. 一种基于多约束的空间聚类方法[J]. 测绘学报, 2011, 40(4):509-516. LIU Qiliang, DENG Min, SHI Yan, et al. A Novel Spatial Clustering Method Based on Multi-constraints[J]. Acta Geodaetica et Cartographica Sinica, 2011, 40(4):509-516.
[14] HAMASUNA Y, ENDO Y, MIYAMOTO S. On Mahalanobis Distance Based Fuzzy C-means Clustering for Uncertain Data Using Penalty Vector Regularization[C]//Proceedings of 2011 IEEE International Conference on Fuzzy Systems. Taipei, Taiwan, China:IEEE, 2011:810-815.
[15] 杨红磊, 彭军还. 基于马尔可夫随机场的模糊C-均值遥感影像分类[J]. 测绘学报, 2012, 41(2):213-218. YANG Honglei, PENG Junhuan. Remote Sensing Classification Based on Markov Random Field and Fuzzy C-Means Clustering[J]. Acta Geodaetica et Cartographica Sinica, 2012, 41(2):213-218.
[16] 张路, 廖明生. 一种顾及上下文的遥感影像模糊聚类[J]. 遥感学报, 2006, 10(1):58-65. ZHANG Lu, LIAO Mingsheng. Contextual Fuzzy Clustering of Remote Sensing Imagery[J]. Journal of Remote Sensing, 2006, 10(1):58-65.
[17] 姜文志, 顾佼佼, 胡文萱, 等. 条件随机场模型的应用研究及改进[J]. 计算机与现代化, 2011(11):55-58. JIANG Wenzhi, GU Jiaojiao, HU Wenxuan, et al. Research on Applications of Conditional Random Fields and Its Improvement[J]. Jisuanji Yu Xiandaihua, 2011(11):55-58.
[18] 李祖传, 马建文, 张睿, 等. 利用SVM-CRF进行高光谱遥感数据分类[J]. 武汉大学学报(信息科学版), 2011, 36(3):306-310. LI Zuchuan, MA Jianwen, ZHANG Rui, et al. Classifying Hyperspectral Data Using Support Vector Machine Conditional Random Field[J]. Geomatics and Information Science of Wuhan University, 2011, 36(3):306-310.
[19] ZHONG Yanfei, LIN Xuemei, ZHANG Liangpei. A Support Vector Conditional Random Fields Classifier with a Mahalanobis Distance Boundary Constraint for High Spatial Resolution Remote Sensing Imagery[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2014, 7(4):1314-1330.
[20] WANG Yang, RAJAPAKSE J C. Contextual Modeling of Functional MR Images with Conditional Random Fields[J]. IEEE Transactions on Medical Imaging, 2006, 25(6):804-812.
[21] SYMONS C T, SAMATOVA N F, KRISHNAMURTHY R, et al. Multi-criterion Active Learning in Conditional Random Fields[C]//Proceedings of the 18th IEEE International Conference on Tools with Artificial Intelligence. Arlington, VA:IEEE, 2006:323-331.
[22] NOMOTO T. Discriminative Sentence Compression with Conditional Random Fields[J]. Information Processing & Management, 2007, 43(6):1571-1587.
[23] ZHANG Guangyun, JIA Xiuping. Simplified Conditional Random Fields with Class Boundary Constraint for Spectral-Spatial Based Remote Sensing Image Classification[J]. IEEE Geoscience and Remote Sensing Letters, 2012, 9(5):856-860.

条件随机场模型约束下的遥感影像模糊C-均值聚类算法

A Modified FCM Classifier Constrained by Conditional Random Field Model for Remote Sensing Imagery

PDF

可视化

被引次数

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 6

编辑推荐

Metrics

本文评价

[1]	魏立飞, 余铭, 钟燕飞, 袁自然, 黄灿. 空-谱融合的条件随机场高光谱影像分类方法[J]. 测绘学报, 2020, 49(3): 343-354.
[2]	赵泉华, 谢凯浪, 王光辉, 李玉. 全卷积网络和条件随机场相结合的全极化SAR土地覆盖分类[J]. 测绘学报, 2020, 49(1): 65-78.
[3]	左宗成, 张文, 张东映. 融合可变形卷积与条件随机场的遥感影像语义分割方法[J]. 测绘学报, 2019, 48(6): 718-726.
[4]	何鄂龙, 王红平, 陈奇, 刘修国. 一种改进的空间上下文点云分类方法[J]. 测绘学报, 2017, 46(3): 362-370.
[5]	万红林,,焦李成,王桂婷,辛芳芳. 基于感兴趣区域在区域层面上决策的SAR图像变化检测的方法研究[J]. 测绘学报, 2012, 41(2): 239-245.
[6]	刘小芳,何彬彬,李小文. 基于半监督核模糊C-均值算法的北京一号小卫星多光谱图像分类[J]. 测绘学报, 2011, 40(3): 301-306.