可变类空间约束高斯混合模型遥感图像分割
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摘要
针对传统高斯混合模型(GMM,Gaussian mixture model)难以自动获取类属数和对噪声敏感问题,提出了一种基于可变类空间约束GMM的遥感图像分割方法。首先在构建的GMM中,将像素类属性建模为马尔可夫随机场(MRF,Markov random field),并在此基础上定义其先验概率;结合邻域像素类属性的后验概率和先验概率,定义噪声平滑因子,以提高算法的抗噪性;在参数求解过程中,分别采用可逆跳变马尔可夫链蒙特卡罗(RJMCMC,reversible jump Markov chain Monte Carlo)方法和最大似然(ML,maximum likelihood)方法估计类属数和模型参数;最后以最小化噪声平滑因子为准则获取最终分割结果。为了验证提出的分割方法,分别对模拟图像和全色遥感图像进行了可变类分割实验。实验结果表明提出方法的可行性和有效性。
In view of the traditional Gaussian mixture model(GMM),it was difficult to obtain the number of classes and sensitive to the noise.A remote sensing image segmentation method based on spatially constrained GMM with unknown number of classes was proposed.First,in the built GMM,prior probability that represented the membership between a pixel and one class was modeled as a Markov random field(MRF).In order to improve the sensitivity of noise,the smoothing factor was defined by combining the a posterior probability and the prior probability of neighboring pixels.For estimating the number of classes and the parameters of model,the reversible jump Markov chain Monte Carlo(RJMCMC) and maximum likelihood(ML) estimation were employed,respectively.Finally,by minimizing the smoothing factor the final segmentation was obtained.In order to verify the proposed segmentation method,the synthetic and real panchromatic images were tested.The experimental results show that the proposed method is feasible and effective.
In view of the traditional Gaussian mixture model(GMM),it was difficult to obtain the number of classes and sensitive to the noise.A remote sensing image segmentation method based on spatially constrained GMM with unknown number of classes was proposed.First,in the built GMM,prior probability that represented the membership between a pixel and one class was modeled as a Markov random field(MRF).In order to improve the sensitivity of noise,the smoothing factor was defined by combining the a posterior probability and the prior probability of neighboring pixels.For estimating the number of classes and the parameters of model,the reversible jump Markov chain Monte Carlo(RJMCMC) and maximum likelihood(ML) estimation were employed,respectively.Finally,by minimizing the smoothing factor the final segmentation was obtained.In order to verify the proposed segmentation method,the synthetic and real panchromatic images were tested.The experimental results show that the proposed method is feasible and effective.
引文
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[3]王露,刘庆元.高分辨率遥感影像多尺度分割中最优尺度选取方法综述[J].测绘与空间地理信息,2015,38(3):166-169.WANG L,LIU Q Y.The methods summary of optimal segmentation scale selection in high-resolution remote sensing images multi-scale segmentation[J].Geomatics&Spatial Information Technology,2015,38(3):166-169.
[4]孔凡壮,蒋艳凰,刘芸.遥感图像分割技术综述[C]//2005通信理论与技术新进展——第十届全国青年通信学术会议.2005:790-792.KONG F Z,JIANG Y H,LIU Y.A survey on remote image segmentation methods[C]//2005 New Progress in Communication Theory and Technology,The 10th National Youth Communication Conference Proceedings.2005:790-792.
[5]赵泉华,赵雪梅,李玉.结合HMRF模型的模糊ISODATA高分辨率遥感图像分割[J].信号处理,2016,32(2):157-166.ZHAO Q H,ZHAO X M,LI Y.A fuzzy ISODATA approach combining hidden Markov random field model for high resolution remote sensing image segmentation[J].Journal of Signal Processing,2016,32(2):157-166.
[6]ZHAO Q H,LI Y,WANG Y.SAR image segmentation with unknown number of classes combined Voronoi tessellation and RJMCMC algorithm[J].ISPRS Annals of Photogrammetry,Remote Sensing and Spatial Information Sciences,2016,12(19):119-124.
[7]MCLACHLAN G J,PEEL D.Finite mixture models[M].New York:John Wiley&Sons,2000:7-35.
[8]卢洁,杨学志,郎文辉.区域GMM聚类的SAR图像分割[J].中国图象图形学报,2011,16(11):2088-2094.LU J,YANG X Z,LANG W H.SAR image segmentation with region-based GMM[J].Journal of Image and Graphics,2011,16(11):2088-2094.
[9]王荔霞,谢维信,裴继红.多高斯模型特征空间覆盖学习的海洋航摄图像分割[J].电子学报,2014,42(10):2117-2122.WANG L X,XIE W X,PEI J H.Aerial ocean image segmentation using multi-Gauss model characteristic space cover leaming[J].Acta Electronica Sinica,2014,42(10):2117-2122.
[10]SANJAY-GOPAL S,HEBERT T J.Bayesian pixel classification using spatially variant finite mixtures and the generalized EM algorithm[J].IEEE Transactions on Image Processing,1998,7(7):1014-1028.
[11]NIKOU C,GALATSANOS N P,LIKAS A C.A class-adaptive spatially variant mixture model for image segmentation[J].IEEE Transactions on Image Processing,2007,16(4):1121-1130.
[12]KATO Z.Segmentation of color images via reversible jump MCMC sampling[J].Journal Image and Vision Computing,2006,26(3):361-371.
[13]HOU Y,YANG Y,RAO N,et al.Mixture model and Markov random field-based remote sensing image unsupervised clustering methed[J].Opto Electronics Review,2011,19(1):83-88.
[14]COMER M,BOUMAN C A,GRAEF M,et al.Bayesian methods for image segmentation[J].Journal of the Minerals,Metals and Materials Society,2011,63(7):55-57.
[15]WU N.The maximum entropy method[M].Springer Science&Business Media,Berlin Heidelberg,2012:1-9.
[16]CHATZIS S P,VARVARIGOU T A.A fuzzy clustering approach toward hidden Markov random field models for enhanced spatially constrained image segmentation[J].IEEE Transactions on Fuzzy Systems,2008,16(5):1351-1361.