改进分数阶达尔文粒子群的多Renyi熵图像分割算法

Multi-Renyi entropy image segmentation algorithm based on improved fractional Darwin particle swarm optimization

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a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 a 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%.