改进分数阶达尔文粒子群的多Renyi熵图像分割算法
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摘要
针对智能优化图像分割算法易陷入局部最优、分割精度不高等问题,本文融合改进的分数阶达尔文粒子群算法和二维Renyi熵多阈值,提出了一种新的多阈值遥感图像分割算法。算法利用粒子自身进化信息来定义进化因子,结合进化因子并利用高斯图函数调整分数阶次α系数以实现精确计算和快速收敛;根据局部最优概率因子对局部最优位置进行Levy飞行随机扰动以提高算法跳出局部最优的能力;同时将二维Renyi熵单阈值扩展到多阈值分割上,并结合改进的分数阶达尔文粒子群算法,将二维Renyi熵多阈值应用于遥感图像分割中。仿真结果表明,与其他2种智能优化分割算法相比,本文分割算法在细节处理和分割精度上均有明显优势,在PRI上至少提升7.27%、VOI至少降低6.5%、GCE至少降低10.4%。
In view of the problem that intelligent optimization image segmentation algorithm is easy to fall into local optimal and low segmentation precision,this paper presents a new multi-threshold remote sensing image segmentation algorithm,which combines the improved fractional Darwin particle swarm optimization and the two-dimensional Renyi entropy multi-threshold. The algorithm uses the particle evolution information to define the evolution factor,combines the evolutionary factor and adjusts the fractional coefficients α by using the Gauss graph function to achieve accurate calculation and fast convergence.According to the local optimal probability factor,the Levy flight random disturbance is carried out to improve the ability of the algorithm to jump out of the local optimal. At the same time,the two-dimensional Renyi entropy single threshold is extended to the multi-threshold segmentation,and the improved fractional Darwin particle swarm optimization is used to apply the two-dimensional Renyi entropy multi-threshold to the remote sensing image segmentation.The simulation results show that,compared with the other two intelligent segmentation algorithms,the segmentation algorithm has obvious advantages in detail processing and segmentation accuracy,at least 7.27% increase in PRI,6.5% decrease in VOI,and decrease in GCE by at least 10.4%.
In view of the problem that intelligent optimization image segmentation algorithm is easy to fall into local optimal and low segmentation precision,this paper presents a new multi-threshold remote sensing image segmentation algorithm,which combines the improved fractional Darwin particle swarm optimization and the two-dimensional Renyi entropy multi-threshold. The algorithm uses the particle evolution information to define the evolution factor,combines the evolutionary factor and adjusts the fractional coefficients α by using the Gauss graph function to achieve accurate calculation and fast convergence.According to the local optimal probability factor,the Levy flight random disturbance is carried out to improve the ability of the algorithm to jump out of the local optimal. At the same time,the two-dimensional Renyi entropy single threshold is extended to the multi-threshold segmentation,and the improved fractional Darwin particle swarm optimization is used to apply the two-dimensional Renyi entropy multi-threshold to the remote sensing image segmentation.The simulation results show that,compared with the other two intelligent segmentation algorithms,the segmentation algorithm has obvious advantages in detail processing and segmentation accuracy,at least 7.27% increase in PRI,6.5% decrease in VOI,and decrease in GCE by at least 10.4%.
引文
[1]董志鹏,王密,李德仁.一种融合超像素与最小生成树的高分辨率遥感影像分割方法[J].测绘学报,2017,46(6):734-742.
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[14]雷博,范九伦.一维Renyi熵阈值法中参数的自适应选取[J].光子学报,2009,38(9):2439-2443.
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[19]杨景明,马明明,车海军,等.多目标自适应混沌粒子群优化算法[J].控制与决策,2015,30(12):2168-2174.
[20]郭森,秦贵和,张晋东,等.多目标车辆路径问题的粒子群优化算法研究[J].西安交通大学学报,2016,50(9):97-104.
[21]贾旋,周治平.一种基于模板缩减的新型粒子群遗传聚类算法[J].智能系统学报,2016,11(4):561-566.
[22]TILLETT J,RAO T,SAHIN F,et al.Darwinian particle swarm optimization[C]∥Proceedings of the 2nd Indian International Conference on Artificial Intelligence.[S.l.]:Rochester Institute of Technology,2005:1474-1487.
[23]PIRES E J S,MACHADO J A T,OLIVEIRA P B D M,et al.Particle swarm optimization with fractional order velocity[J].Nonlinear Dynamics,2010,61(1-2):295-301.
[24]COUCEIRO M S.Introducing the fractional-order Darwinian PSO[J].Signal Image&Video Processing,2012,6(3):343-350.
[25]魏晶茹,马瑜,夏瑞,等.基于分数阶粒子群的Otsu图像分割算法[J].计算机工程与设计,2017(12):3284-3290.
[26]余胜威,曹中清.基于分数阶达尔文粒子群FODPSO算法的图像分割[J].计算机工程与科学,2016,38(9):1836-1842.
[27]葛亮,杨竣铎.基于蚁群优化多层图划分的彩色图像分割方法[J].计算机应用研究,2015,32(4):1265-1269.
[2]文政颖,于海鹏.基于多Gamma分布模型的SAR图像直方图分割算法[J].计算机工程与设计,2014,35(6):2104-2108.
[3]陈祥,孙俊,尹奎英,等.基于Otsu与海域统计特性的SAR图像海陆分割算法[J].数据采集与处理,2014,29(4):603-608.
[4]MA X,LIU S,HU S,et al.SAR image edge detection via sparse representation[J].Soft Computing,2017:1-9.
[5]李淑清,李瑞华,王潇.基于SAR图像的海洋溢油分割方法研究[J].测绘工程,2017,26(2):37-44.
[6]SHI Z,LIHUANG S,LI L,et al.A modified fuzzy C-means for bias field estimation and segmentation of brain MRimage[C]∥Proceedings of the 25th Chinese Control and Decision Conference(CCDC).Guiyang:IEEE,2013:2080-2085.
[7]杨晓波.结合小波变换和K均值聚类的SAR舰船目标分割[J].舰船科学技术,2016(9X):169-171.
[8]汪华章,宰文姣.改进型脉冲耦合神经网络高分辨率SAR图像分割[J].北京大学学报(自然科学版),2013,49(2):57-63.
[9]CHEN L C,PAPAND R EOU G,KOKKINOS I,et al.Semantic image segmentation with deep convolutional nets and fully connected CRFs[J].Computer Science,2016(4):357-361.
[10]MALMGREN-HANSEN D,NOBEL J M.Convolutional neural networks for SAR image segmentation[C]∥2015 IEEEInternational Symposium on Signal Processing and Information Technology(ISSPIT).Abu Dhabi,United Arab Emirates:IEEE,2015:231-236.
[11]BASELICE F,FERRAIOLI G.Unsupervised coastal line extraction from SAR images[J].IEEE Geoscience&Remote Sensing Letters,2013,10(6):1350-1354.
[12]SAHOO P K,ARORA G.A thresholding method based on two-dimensional Renyi’s entropy[J].Pattern Recognition,2004,37(6):1149-1161.
[13]龚劬,王菲菲,倪麟.基于分解的二维Renyi灰度熵的图像阈值分割[J].计算机工程与应用,2013,49(1):181-185.
[14]雷博,范九伦.一维Renyi熵阈值法中参数的自适应选取[J].光子学报,2009,38(9):2439-2443.
[15]马英辉,吴一全.基于二维Renyi交叉熵的刀具磨损图像分割[J].电子测量与仪器学报,2016,30(12):1869-1876.
[16]李晓峰,方龙.基于二维Renyi熵与自适应人工鱼群算法的红外图像分割[J].舰船电子工程,2016,36(7):109-113.
[17]WU Yiquan,SONG Yu,ZHOU Huaichun.State identification of boiler combustion flame images based on gray entropy multiple thresholding and support vector machine[J].Proceedings of the CSEE,2013,33(20):66-73.
[18]KENNEDY J,EBERHART R.Particle swarm optimization[C]∥Proceedings of IEEE International Conference on Neural Networks.[S.l.]:IEEE,1995:1942-1948.
[19]杨景明,马明明,车海军,等.多目标自适应混沌粒子群优化算法[J].控制与决策,2015,30(12):2168-2174.
[20]郭森,秦贵和,张晋东,等.多目标车辆路径问题的粒子群优化算法研究[J].西安交通大学学报,2016,50(9):97-104.
[21]贾旋,周治平.一种基于模板缩减的新型粒子群遗传聚类算法[J].智能系统学报,2016,11(4):561-566.
[22]TILLETT J,RAO T,SAHIN F,et al.Darwinian particle swarm optimization[C]∥Proceedings of the 2nd Indian International Conference on Artificial Intelligence.[S.l.]:Rochester Institute of Technology,2005:1474-1487.
[23]PIRES E J S,MACHADO J A T,OLIVEIRA P B D M,et al.Particle swarm optimization with fractional order velocity[J].Nonlinear Dynamics,2010,61(1-2):295-301.
[24]COUCEIRO M S.Introducing the fractional-order Darwinian PSO[J].Signal Image&Video Processing,2012,6(3):343-350.
[25]魏晶茹,马瑜,夏瑞,等.基于分数阶粒子群的Otsu图像分割算法[J].计算机工程与设计,2017(12):3284-3290.
[26]余胜威,曹中清.基于分数阶达尔文粒子群FODPSO算法的图像分割[J].计算机工程与科学,2016,38(9):1836-1842.
[27]葛亮,杨竣铎.基于蚁群优化多层图划分的彩色图像分割方法[J].计算机应用研究,2015,32(4):1265-1269.