结合MST划分和RHMRF-FCM算法的高分辨率遥感图像分割
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摘要
针对基于像素的HMRF-FCM算法抗噪性差以及对地物复杂边界分割精度低的问题,提出一种结合形状信息的静态MST区域划分和RHMRF-FCM算法的高分辨率遥感图像分割方法。该方法定义一种静态MST同质区域划分准则,借助MST能较好表达边界和形状信息、能较好抑制几何噪声的特点,解决地物复杂边界的表达和降低分割结果中几何噪声问题。首先,利用MST静态划分将图像域划分成若干个均质区域,假设每个均质区域内光谱测度服从独立同一的多元高斯分布。然后,在此基础上构建了区域隐马尔可夫随机场模型,以及建立基于信息熵和KL信息正则化项的模糊聚类目标函数。最后,采用偏微分方法对分割模型参数进行求解,从而得到全局最优分割结果。为验证本文方法,对WorldView-3高分遥感图像进行分割试验。定性、定量分析了尺度参数、光谱相似性参数和区域紧致度参数对最优分割结果的影响,并对比分析本文算法和eCognition软件中的多分辨率分割算法、分水岭算法。
It is proposed that a high-resolution remote sensing image segmentation method that combines static minimum spanning tree tessellation considering shape information and the RHMRF-FCM algorithm.It solves the problems in traditional pixel-based HMRF-FCM algorithm in which poor noise resistance and low precision segmentation in complex boundary exist.By using the MST model and shape information,the object boundary and geometrical noise can be expressed and reduced respectively.Firstly,the static MST tessellation is employed for partitioning the image domain into some polygons corresponded to the components of homogeneous regions needed to be segmented.Secondly,based on the tessellation results,the RHMRF model is built,and regulation term considering the KL information and information entropy are introduced into the FCM objective function.Finally,the partial differential method is employed to calculate the parameters of the fuzzy objective function for obtaining the global optimal segmentation results.To verify the robust and effective of proposed algorithm,the experiments are carried out with WorldView-3high resolution image.The results from proposed method with different parameters and comparing methods(the multi-resolution and the watershed segmentation method in eCognition software)are analyzed qualitatively and quantitatively.
It is proposed that a high-resolution remote sensing image segmentation method that combines static minimum spanning tree tessellation considering shape information and the RHMRF-FCM algorithm.It solves the problems in traditional pixel-based HMRF-FCM algorithm in which poor noise resistance and low precision segmentation in complex boundary exist.By using the MST model and shape information,the object boundary and geometrical noise can be expressed and reduced respectively.Firstly,the static MST tessellation is employed for partitioning the image domain into some polygons corresponded to the components of homogeneous regions needed to be segmented.Secondly,based on the tessellation results,the RHMRF model is built,and regulation term considering the KL information and information entropy are introduced into the FCM objective function.Finally,the partial differential method is employed to calculate the parameters of the fuzzy objective function for obtaining the global optimal segmentation results.To verify the robust and effective of proposed algorithm,the experiments are carried out with WorldView-3high resolution image.The results from proposed method with different parameters and comparing methods(the multi-resolution and the watershed segmentation method in eCognition software)are analyzed qualitatively and quantitatively.
引文
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[6]赵泉华,李晓丽,赵雪梅,等.结合Voronoi划分HMRF模型的模糊ISODATA图像分割[J].信号处理,2016,32(10):1233-1243.ZHAO Quanhua,LI Xiaoli,ZHAO Xuemei,et al.Fuzzy ISODATA image segmentation integrating Voronoi tessellation HMRF Model[J].Journal of Signal Processing,2016,32(10):1233-1243.
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[8]黎莹,戴芳,郝勇,等.基于最小生成树的图像分割[J].计算机工程与应用,2013,49(13):149-151.LI Ying,DAI Fang,HAO Yong,et al.Image segmentation based on minimum spanning tree[J]. Computer Engineering and Applications,2013,49(13):149-151.
[9] PENG Bo,ZHANG Lei,ZHANG D.A survey of graph theoretical approaches to image segmentation[J].Pattern Recognition,2013,46(3):1020-1038.
[10]巫兆聪,胡忠文,张谦,等.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,ZHANG Qian,et al.On combining spectral,textural and shape features for remote sensing image segmentation[J].Acta Geodaetica et Cartographica Sinica,2013,42(1):44-50.
[11]李慧,唐韵玮,刘庆杰,等.一种改进的基于最小生成树的遥感影像多尺度分割方法[J].测绘学报,2015,44(7):791-796.DOI:10.11947/j.AGCS.2015.20140060.LI Hui,TANG Yunwei,LIU Qingjie,et al.An improved algorithm based on minimum spanning tree for multi-scale segmentation of remote sensing imagery[J].Acta Geodaetica et Cartographica Sinica,2015,44(7):791-796.DOI:10.11947/j.AGCS.2015.20140060.
[12] ZAHN C T.Graph-theoretical methods for detecting and describing gestalt clusters[J].IEEE Transactions on Computers,1971,C-20(1):68-86.
[13]王平,魏征,崔卫红,等.一种基于统计学习理论的最小生成树图像分割准则[J].武汉大学学报(信息科学版),2017,42(7):877-883.WANG Ping,WEI Zheng,CUI Weihong,et al.An image segmentation method based on statistics learning theory and minimum spanning tree[J]. Geomatics and Information Science of Wuhan University,2017,42(7):877-882.
[14] FELZENSZWALB P F,HUTTENLOCHER D P.Efficient graph-based image segmentation[J].International Journal of Computer Vision,2004,59(2):167-181.
[15]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.ZHAO Xuemei,LI Yu,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.
[16]赵泉华,李晓丽,赵雪梅,等.结合马氏距离的区域化模糊聚类遥感图像分割[J].中国矿业大学学报,2017,46(1):222-228.ZHAO Quanhua,LI Xiaoli,ZHAO Xuemei,et al.Remote sensing image segmentation algorithm with regional fuzzy cluster and Mahalanobis distance[J].Journal of China University of Mining&Technology,2017,46(1):222-228.
[17] LUCARINI V.Symmetry-break in Voronoi tessellations[J].Symmetry,2009,1(1):21-54.
[18] SCHNEIDER R.Weighted faces of Poisson hyperplane tessellations[J].Advances in Applied Probability,2009,41(3):682-694.
[19] ACHANTA R,SHAJI A,SMITH K,et al.SLIC superpixels compared to state-of-the-art superpixel methods[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2012,34(11):2274-2282.
[20] BANERJEE B,VARMA S,BUDDHIRAJU K M,et al.Unsupervised multi-spectral satellite image segmentation combining modified mean-shift and a new minimum spanning tree based clustering technique[J].IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,2014,7(3):888-894.
[21] CUI Weihong,ZHANG Yi.An effective graph-based hierarchy image segmentation[J].Intelligent Automation&Soft Computing,2011,17(7):969-981.
[22] NARAYAN D,MURTHY S,KUMAR G H.Image segmentation based on graph theoretical approach to improve the quality of image segmentation[J].Proceedings of World Academy of Science,Engineering and Technology,2008,18(1):35-38.
[23]杨佳学.大规模图上的最小生成树并行算法研究[D].沈阳:东北大学,2013.YANG Jiaxue.Research on the parallel algorithm of the minimum spanning tree on large scale graphs[D].Shenyang:Northeastern University,2013.
[24] 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.
[25] CONGALTON R G,GREEN K.Assessing the accuracy of remotely sensed data:principles and practices[M].Boca Raton:CRC Press,2008:169-190.
[2]李德仁,童庆禧,李荣兴,等.高分辨率对地观测的若干前沿科学问题[J].中国科学(地球科学),2012,42(6):805-813.LI Deren,TONG Qingxi,LI Rongxing,et al.Current issues in high-resolution Earth observation technology[J].Science China:Earth Sciences,2012,42(6):805-813.
[3]洪亮.基于对象马尔可夫模型的高分辨率遥感影像分割方法研究[D].武汉:武汉大学,2010.HONG Liang.Research on the segmentation of high resolution remote sensing image based on object-oriented Markovian random fields model[D].Wuhan:Wuhan University,2010.
[4] ASKARI G,XU Aigong,LI Yu,et al.Automatic determination of number of homogenous regions in SAR images utilizing splitting and merging based on a reversible jump MCMC algorithm[J].Journal of the Indian Society of Remote Sensing,2013,41(3):509-521.
[5]王玉,李玉,赵泉华.结合规则划分和M-H算法的SAR图像分割[J].武汉大学学报(信息科学版),2016,41(11):1491-1497.WANG Yu,LI Yu,ZHAO Quanhua.SAR image segmentation combined regular tessellation and M-H algorithm[J].Geomatics and Information Science of Wuhan University,2016,41(11):1491-1497.
[6]赵泉华,李晓丽,赵雪梅,等.结合Voronoi划分HMRF模型的模糊ISODATA图像分割[J].信号处理,2016,32(10):1233-1243.ZHAO Quanhua,LI Xiaoli,ZHAO Xuemei,et al.Fuzzy ISODATA image segmentation integrating Voronoi tessellation HMRF Model[J].Journal of Signal Processing,2016,32(10):1233-1243.
[7] WANG Ping,WEI Zheng,CUI Weihong,et al.A minimum spanning tree based method for UAV image segmentation[C]∥ISPRS Annals of Photogrammetry,Remote Sensing and Spatial Information Sciences.Prague:[s.n.],2016:111-117.
[8]黎莹,戴芳,郝勇,等.基于最小生成树的图像分割[J].计算机工程与应用,2013,49(13):149-151.LI Ying,DAI Fang,HAO Yong,et al.Image segmentation based on minimum spanning tree[J]. Computer Engineering and Applications,2013,49(13):149-151.
[9] PENG Bo,ZHANG Lei,ZHANG D.A survey of graph theoretical approaches to image segmentation[J].Pattern Recognition,2013,46(3):1020-1038.
[10]巫兆聪,胡忠文,张谦,等.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,ZHANG Qian,et al.On combining spectral,textural and shape features for remote sensing image segmentation[J].Acta Geodaetica et Cartographica Sinica,2013,42(1):44-50.
[11]李慧,唐韵玮,刘庆杰,等.一种改进的基于最小生成树的遥感影像多尺度分割方法[J].测绘学报,2015,44(7):791-796.DOI:10.11947/j.AGCS.2015.20140060.LI Hui,TANG Yunwei,LIU Qingjie,et al.An improved algorithm based on minimum spanning tree for multi-scale segmentation of remote sensing imagery[J].Acta Geodaetica et Cartographica Sinica,2015,44(7):791-796.DOI:10.11947/j.AGCS.2015.20140060.
[12] ZAHN C T.Graph-theoretical methods for detecting and describing gestalt clusters[J].IEEE Transactions on Computers,1971,C-20(1):68-86.
[13]王平,魏征,崔卫红,等.一种基于统计学习理论的最小生成树图像分割准则[J].武汉大学学报(信息科学版),2017,42(7):877-883.WANG Ping,WEI Zheng,CUI Weihong,et al.An image segmentation method based on statistics learning theory and minimum spanning tree[J]. Geomatics and Information Science of Wuhan University,2017,42(7):877-882.
[14] FELZENSZWALB P F,HUTTENLOCHER D P.Efficient graph-based image segmentation[J].International Journal of Computer Vision,2004,59(2):167-181.
[15]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.ZHAO Xuemei,LI Yu,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.
[16]赵泉华,李晓丽,赵雪梅,等.结合马氏距离的区域化模糊聚类遥感图像分割[J].中国矿业大学学报,2017,46(1):222-228.ZHAO Quanhua,LI Xiaoli,ZHAO Xuemei,et al.Remote sensing image segmentation algorithm with regional fuzzy cluster and Mahalanobis distance[J].Journal of China University of Mining&Technology,2017,46(1):222-228.
[17] LUCARINI V.Symmetry-break in Voronoi tessellations[J].Symmetry,2009,1(1):21-54.
[18] SCHNEIDER R.Weighted faces of Poisson hyperplane tessellations[J].Advances in Applied Probability,2009,41(3):682-694.
[19] ACHANTA R,SHAJI A,SMITH K,et al.SLIC superpixels compared to state-of-the-art superpixel methods[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2012,34(11):2274-2282.
[20] BANERJEE B,VARMA S,BUDDHIRAJU K M,et al.Unsupervised multi-spectral satellite image segmentation combining modified mean-shift and a new minimum spanning tree based clustering technique[J].IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,2014,7(3):888-894.
[21] CUI Weihong,ZHANG Yi.An effective graph-based hierarchy image segmentation[J].Intelligent Automation&Soft Computing,2011,17(7):969-981.
[22] NARAYAN D,MURTHY S,KUMAR G H.Image segmentation based on graph theoretical approach to improve the quality of image segmentation[J].Proceedings of World Academy of Science,Engineering and Technology,2008,18(1):35-38.
[23]杨佳学.大规模图上的最小生成树并行算法研究[D].沈阳:东北大学,2013.YANG Jiaxue.Research on the parallel algorithm of the minimum spanning tree on large scale graphs[D].Shenyang:Northeastern University,2013.
[24] 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.
[25] CONGALTON R G,GREEN K.Assessing the accuracy of remotely sensed data:principles and practices[M].Boca Raton:CRC Press,2008:169-190.
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