检索规则说明:AND代表“并且”;OR代表“或者”;NOT代表“不包含”;(注意必须大写,运算符两边需空一格)
检 索 范 例 :范例一: (K=图书馆学 OR K=情报学) AND A=范并思 范例二:J=计算机应用与软件 AND (U=C++ OR U=Basic) NOT M=Visual
出 处:《中国电子科学研究院学报》2007年第3期228-233,共6页Journal of China Academy of Electronics and Information Technology
摘 要:独立分量分析作为盲源信号分离的一种有效的方法在许多方面获得成功应用。讨论了独立分量分析的基本原理、目标函数选择和算法,并在此基础上,对快速独立分析算法FastICA的核心迭代过程进行改进,得到M-FastICA算法,改进算法减少了独立分量分析的迭代次数,从而提高了算法的收敛速度。最后将M-FastICA算法应用到遥感图像的融合上,实验结果表明,改进算法在融合效果相当的前提下,收敛速度更快。Independent Component Analysis (ICA) is an effective approach to the separation of blind signal, and has attracted broad attention and has been successfully used in many fields. The fundamental, target functions and practical algorithm of Independent Component Analysis are discussed. Then, by modifying the performance of FastICA algorithm are merged into one iteration of M-FastICA. The M-Fas- tICA algorithm achieves the correspondent effect of FastICA. So the convergence of ICA will be accelerated. Finally, M-FastICA is applied to images fusion. The experiment results show that the modified algorithm reduces iterations with the correspondent fusion performance.
关 键 词:独立分量分析 FASTICA M-FastICA 图像融合
分 类 号:TN957.52[电子电信—信号与信息处理]
正在载入数据...
正在载入数据...
正在载入数据...
正在载入数据...
正在载入数据...
正在载入数据...
正在载入数据...
正在链接到云南高校图书馆文献保障联盟下载...
云南高校图书馆联盟文献共享服务平台 版权所有©
您的IP:216.73.216.46