摘要
为解决经典聚类图像分割算法对聚类中心的依赖性和图像噪声的敏感性问题,提出了一种基于信息聚类的遥感图像分割方法.利用Gaussian分布建立同质区域内像素的概率分布,即假设每个同质区域内的像素都服从同一独立的Gaussian分布;结合Gaussian分布的特性建立像素对间灰度的联合分布.在此基础上,以互信息作为聚类算法的相似性测度,结合同质区域内以及同质区域间像素灰度的相似性建立目标函数,通过最大化求解上述目标函数,进而转化为迭代求解像素与同质区域的隶属度实现遥感图像分割.分别对模拟及真实遥感图像进行分割实验.结果表明:该方法不仅避免了聚类中心的选取,还降低了噪声敏感性,并且增强了图像分割的稳定性,从而验证了该方法的可行性及有效性.
A new algorithm based on information clustering is presented for remote sensing （RS） image segmentation, which solves the dependency of clustering centers and the sensitive- to-noise problem in the classical clustering image segmentation methods. The intensities of the homogenous region of RS image were assumed to satisfy identical and independent Gaussian distributions. Combining with the characteristics of Gaussian distribution, the joint distribu- tion of pair-wise pixels was established. The objective function was formed based on the mutual information used as similarity measure in clustering process, and the pixel similarity in and be- tween homogeneous regions. The iterative solution of membership between the pixel and hom- ogeneous regions is equivalent to the maximizing solution of the objective function, so as to a- chieve RS image segmentation. Experiments on simulated and real images were performed to il- lustrate the efficiency and effectiveness of the proposed algorithm. Results show that the new method can avoid the initial clustering center selection, reduce the noise sensitivity and enhance the stability of image segmentation, which verifies the feasibility and effectiveness of the pro- posed algorithm.
出处
《中国矿业大学学报》
EI
CAS
CSCD
北大核心
2017年第1期209-214,共6页
Journal of China University of Mining & Technology
基金
国家自然科学基金青年基金项目(41301479)
国家自然科学基金面上项目(41271435)
辽宁省自然科学基金项目(2015020190)
关键词
互信息
聚类中心
图像噪声
相似性测度
遥感图像分割
mutual information
clustering center
image noise
similarity measure
remote sensing image segmentation
