摘要
高光谱检测是物质定性识别的重要手段,光谱解混是高光谱分析识别的关键。针对化合物或矿物混合光谱分析不准确的问题,采用非负矩阵分解(NMF)盲源解混方法,建立了一种基于加权NMF高光谱反射曲线的盲源解混分离方法,用于矿物混合后高光谱的分解与识别。假设混合光谱模型是多种组分光谱按比例组合的线性方程,该算法以混合光谱与组分光谱基向量光谱角余弦值为初始权,采用最小欧氏距离和重加权稀疏约束来建立组合条件从而促进解混矩阵的稀疏性,开展方程的NMF约束迭代计算,最终分解出矿物混合光谱的源光谱基向量和丰度矩阵。选取化学纯的氧化铜和氧化亚铜、碱式碳酸铜和氢氧化铜、孔雀石和蓝铜矿三类混合物的高光谱曲线为试验对象,经过均值化和白化等数据预处理后,进行基于加权NMF高光谱反射曲线的盲源解混试验,并以解混性能指数、光谱均方根误差和光谱角距离为评价指标分析算法的解混效果。结果表明,NMF解混方法的盲源解混效果十分明显,在未知混合光谱先验条件基础上,可以准确分离出源光谱特征,样本分离精度均小于0.15。解混后光谱与源光谱的曲线整体变化趋势相同,保持了源光谱相似的吸收位置和吸收峰,但是对应吸收位置存在微小偏移,解混后光谱与源光谱在反射率数值上存在明显的差异。对混合光谱数据加入5%~15%的高斯噪声后,再进行基于加权NMF解混处理。发现混合光谱解混分离的精度随着噪声增大只有微小减小,解混后光谱角距离以及均方根误差并未发生明显的变化,说明NMF解混算法具有较好抗噪性能,对实测非纯物质光谱解混具有一定适用性,可以作为矿物混合后组分识别与分离鉴定的基础方法。
Hyperspectral detection is an important method for qualitatively identifying substances,and spectral unmixing is the key to hyperspectral analysis and identification.The blind source unmixing separation method based on weighted non-negative matrix factorization(NMF)hyperspectral reflection curves are established for the spectral decomposition and identification of minerals after mixing by using the NMF blind source unmixing method to address the problem of inaccurate analysis of compound or mineral mixed spectral in the paper.The algorithm assumes that the spectral mixing model is a linear combination of scaled component spectral signals,uses the minimum Euclidean distance and reweighted sparsity constraints to establish the combination conditions to promote the sparsity of the unmixing matrix,and carries out the iterative calculation of the unmixing NMF constraint with the initial weight of the spectral angular cosine of the mixing spectra and component spectral basis vectors to finally decompose the source spectral basis vectors and the abundance matrix of the mineral mixing spectral.Three mixtures of chemically pure CuO and Cu 2O,Cu(OH)2 and Cu 2(OH)2CO 3,malachite and azurite hyperspectral profiles were selected for spectral unmixing and identification experiments.After the measured mixture spectral curves were equalized and whitened,the blind source unmixing calculation based on the weighted NMF hyperspectral reflection curve was carried out,and the unmixing performance index PI,the root mean square error of the spectral and the angular distance of the spectral were selected as the evaluation indexes of the unmixing effect.The experimental results show that the blind source unmixing effect of the NMF unmixing method is very obvious,the base source spectral features can be accurately separated based on unknown mixed spectral a priori conditions.The sample separation accuracy is less than 0.15.The curves of the de-mixed and the source spectral have the same overall trend,maintaining similar absorption positions and absorption peaks of the source spectral,with minor shifts and obvious differences in reflectance values of the corresponding absorption positions.After adding 5%~15%Gaussian noise to the mixed spectral data,a weighted NMF-based unmixing process was performed,and it was found that the unmixing separation accuracy decreased slightly as the noise increased.However,the overall angular distance of the spectral and the root mean square error did not change significantly after unmixing,indicating that the NMF unmixing algorithm has good noise immunity and applies to spectra unmixing of measured non-pure material,which provides a basic theory for the identification and separation of mineral components after mixing.
作者
汪金花
戴佳乐
李孟倩
刘巍
缪若梵
WANG Jin-hua;DAI Jia-le;LI Meng-qian;LIU Wei;MIAO Ruo-fan(College of Mining Engineering,North China University of Science and Techonlogy,Tangshan 063210,China)
出处
《光谱学与光谱分析》
SCIE
EI
CAS
CSCD
北大核心
2023年第8期2458-2466,共9页
Spectroscopy and Spectral Analysis
基金
国家自然科学基金面上项目(51774140)
河北省自然科学基金项目(E2021209147)
河北省高等学校科学技术研究重点项目(ZD2021082)
科技基础研究项目(JQN2020037)资助。
关键词
混合光谱曲线
光谱解混算法
光谱NMF盲源解混
解混性能指数
Spectral mixing
Spectral de-mixing algorithm
Spectral NMF blind source de-mixing
De-mixing performance index
