激光与光电子学进展, 2019, 56 (9): 091001, 网络出版: 2019-07-05  

基于数据简化的改进非负矩阵分解端元提取方法 下载: 943次

Improved Algorithm for Nonnegative Matrix Factorization and Endmember Extraction Based on Data Simplification
作者单位
1 西安航空学院电子工程学院, 陕西 西安 710077
2 西北大学城市与环境学院, 陕西 西安 710127
3 西安石油大学计算机学院, 陕西 西安 710065
摘要
提出了一种基于高光谱数据简化的改进非负矩阵分解端元提取方法,通过计算和比较图像的光谱信息熵,划分图像的同质区,只选择同质区中最具代表性的像元参与非负矩阵分解运算,减少了端元提取算法的运算量。实验结果显示,数据简化前后运用非负矩阵分解算法所提取的几种矿物的光谱角均值基本相等,但数据简化后端元提取算法的运行时间减少了4/5,算法的运行效率提高。
Abstract
An improved method for nonnegative matrix decomposition and endmember extraction is proposed based on hyperspectral data simplification. Further, the homogeneous regions of images can be identified by calculating and comparing the spectral information entropy of various regions. Only the most representative pixels in the homogeneous regions are selected for application in the subsequent nonnegative matrix decomposition algorithm, which considerably reduces the amount of computation required in the endmember extraction algorithm. The experimental results show that although the mean values of the spectral angles of several kinds of minerals extracted using the nonnegative matrix factorization algorithm before and after data simplification are equal, the operation time of endmember extraction after data simplification is reduced by approximately 4/5, and the operating efficiency of the algorithm is improved.

徐君, 王旭红, 王彩玲. 基于数据简化的改进非负矩阵分解端元提取方法[J]. 激光与光电子学进展, 2019, 56(9): 091001. Jun Xu, Xuhong Wang, Cailing Wang. Improved Algorithm for Nonnegative Matrix Factorization and Endmember Extraction Based on Data Simplification[J]. Laser & Optoelectronics Progress, 2019, 56(9): 091001.

引用该论文: TXT   |   EndNote

相关论文

加载中...

关于本站 Cookie 的使用提示

中国光学期刊网使用基于 cookie 的技术来更好地为您提供各项服务,点击此处了解我们的隐私策略。 如您需继续使用本网站,请您授权我们使用本地 cookie 来保存部分信息。
全站搜索
您最值得信赖的光电行业旗舰网络服务平台!