摘要
非负矩阵分解(Nonnegative Matrix Factorization,NMF)技术已经成为了高光谱解混领域研究的热点。但是如何有效地利用高光谱的空间和光谱信息仍然是一个难点,尤其在确定局部邻域时,往往会遇到结构固定等问题。针对以上问题,提出了一种基于自适应局部邻域加权约束的非负矩阵分解算法。算法根据丰度的数据特点可以自适应确定给定像元的局部邻域,算法中的权重充分地利用了给定像元和邻域内像元的空间和光谱信息,改善了高光谱解混的性能。论文采用梯度下降法推导出乘法迭代规则,为验证所提出的算法的有效性,利用Japser Ridge数据集和Urban数据集进行实验,并与其他经典方法进行对比,结果显示该方法具有更好的解混效果。
Nonnegative Matrix Factorization(NMF)had become a hot research topic in the field of hyperspectral unmixing.However,it was still difficult to make effective use of hyperspectral space and spectral information,especially when determining local neighborhood,structure fixation was often encountered.To solve the above problems,a non-negative matrix decomposition algorithm based on adaptive local neighborhood weighting constraint was proposed.The local neighborhood of a given pixel could be determined adaptively according to the data characteristics of the abundance.The weight of the algorithm made full use of the spatial and spectral information of the given pixel and the pixel in the neighborhood to improve the performance of hyperspectral unmixing.In this paper,the iterative rule of multiplication was derived by gradient descent method.In order to verify the effectiveness of the proposed algorithm,Japser Ridge data set and Urban data set were used for experiments,and compared with other classical methods,the results showed that this method had better unmixing effect.
作者
陈善学
吕俊杰
CHEN Shanxue;LYU Junjie(Chongqing Key Laboratory of Mobile Communications Technology,Engineering Research Center of Mobile Communications of the Ministry of Education,School of Communication and Information Engineering,Chongqing University of Posts and Telecommunications,Chongqing 400065,China)
出处
《信号处理》
CSCD
北大核心
2021年第5期804-813,共10页
Journal of Signal Processing
基金
国家自然科学基金(61271260)
重庆市教委科学技术研究项目(KJ1400416)资助课题。
关键词
高光谱解混
非负矩阵分解
自适应
局部邻域
权重
hyperspectral unmixing
adaptive
non-negative matrix factorization
local neighborhood
weight
