摘要
为了解决实际高光谱解混(HU)中噪声对解混精度的影响和光谱、空间信息利用不足的问题,提出了一种改进的基于光谱距离聚类的群稀疏非负矩阵分解的解混算法。首先,引入了基于最小误差的高光谱信号辨识算法(Hysime),通过计算特征值的方式估计信号矩阵和噪声矩阵;然后,提出了一种简单的基于光谱距离的聚类算法,对多个波段生成的光谱反射率距离值小于某一值的相邻像元进行合并聚类生成空间群结构;最后,在生成的群结构基础上进行稀疏化非负矩阵分解。实验分析表明,对于模拟数据和实际数据而言,该算法都比传统算法产生更小的均方根误差(RMSE)和光谱角距离(SAD),能够产生优于同类算法的解混效果。
In order to solve the problem of the effect of noise on the unmixing precision and the insufficient utilization of spectral and spatial information in the actual Hyperspectral Unmixing(HU),an improved unmixing algorithm based on spectral distance clustering for group sparse nonnegative matrix factorization was proposed.Firstly,the HYperspectral Signal Identification by Minimum Error(Hysime)algorithm for the large amount of noise existing in the actual hyperspectral image was introduced,and the signal matrix and the noise matrix were estimated by calculating the eigenvalues.Then,a simple clustering algorithm based on spectral distance was proposed and used to merge and cluster the adjacent pixels generated by multiple bands,whose spectral reflectance distances are less than a certain value,to generate the spatial group structure.Finally,sparse non-negative matrix factorization was performed on the basis of the generated group structure.Experimental analysis shows that for both simulated data and actual data,the algorithm produces smaller Root-Mean-Square Error(RMSE)and Spectral Angle Distance(SAD)than traditional algorithms,and can produce better unmixing effect than other advanced algorithms.
作者
刘颖
梁楠楠
李大湘
杨凡超
LIU Ying;LIANG Nannan;LI Daxiang;YANG Fanchao(College of Communication and Information Engineering,Xi’an University of Posts and Telecommunications,Xi’an Shaanxi 710121,China;Key Laboratory of Electronic Information and Application Technology for Scene Investigation,Ministry of Public Security(Xi’an University of Posts&Telecommunications),Xi’an Shaanxi 710121,China;Key Laboratory of Spectral Imaging Technique,Xi’an Institute of Optics and Precision Mechanics,Chinese Academy of Sciences,Xi’an Shaanxi 710119,China)
出处
《计算机应用》
CSCD
北大核心
2019年第9期2541-2546,共6页
journal of Computer Applications
基金
国家自然科学基金资助项目(61571361)
陕西省国际合作交流项目(2017KW-013)
西安邮电大学研究生创新基金资助项目(CXJJLY2018024)~~
关键词
高光谱解混
基于最小误差的高光谱信号辨识算法
光谱距离度量
非负矩阵分解
遥感
Hyperspectral Unmixing(HU)
Hyperspectral signal identification algorithm by minimum error(Hysime)
spectral distance metric
Non-negative Matrix Factorization(NMF)
remote sensing
