非负矩阵分解基向量分析及其在矿产预测中的应用

针对矿产预测数据具有复杂性以及矿质异常信息具有稀疏性的问题,基于非负矩阵分解的非负性和降维的特点,结合稀疏性,提出一种基于NMF基向量分析的矿产预测数据处理方法,并对基向量和原变量以及基向量之间的关系进行分析。广东新寮岽铜多金属矿区数据实验结果表明,NMF方法在不同特征值和相似稀疏度的条件下的基向量形态基本稳定,在保留找矿信息的同时可有效地实现对矿产预测数据的稀疏化。NMF方法对于矿产预测具有重要的实际意义。     

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Structured Sparse Method for Hyperspectral Unmixing

Hyperspectral Unmixing (HU) has received increasing attention in the past decades due to its ability of unveiling information latent in hyperspectral data. Unfortunately, most existing methods fail to take advantage of the spatial information in data. To overcome this limitation, we propose a Structured Sparse regularized Nonnegative Matrix Factorization (SS-NMF) method based on the following two aspects. First, we incorporate a graph Laplacian to encode the manifold structures embedded in the hyperspectral data space. In this way, the highly similar neighboring pixels can be grouped together. Second, the lasso penalty is employed in SS-NMF for the fact that pixels in the same manifold structure are sparsely mixed by a common set of relevant bases. These two factors act as a new structured sparse constraint. With this constraint, our method can learn a compact space, where highly similar pixels are grouped to share correlated sparse representations. Experiments on real hyperspectral data sets with different noise levels demonstrate that our method outperforms the state-of-the-art methods significantly.     

Reducing wind noise in seismic data using Non-negative Matrix Factorization: an application to Villarrica volcano,Chile

Single channel source separation of seismic signals is an appealing but difficult problem. In this paper, we introduce a semi-blind single-channel seismic source separation method to enhance the components of volcanic origin. In this method, the source decomposition scheme is addressed as a Sparse Non-negative Matrix Factorization (NMF) of the time-frequency representation of the single vertical seismic channel. As a case study we present an application using seismic data recorded at Villarrica volcano, Chile, one of the most active in the southern Andes. The analysed dataset is strongly contaminated by wind noise and the procedure is used to separate a component of volcanic origin from another of meteorological origin.     

空间与谱间相关性分析的NMF高光谱解混

非负矩阵分解(NMF)技术是高光谱像元解混领域的研究热点。为了充分利用高光谱图像中丰富的空间与光谱相关性特征,改善基于NMF的高光谱解混算法性能,提出一种结合了空间与谱间相关性分析的NMF解混算法。算法针对NMF的通用性和局部极小问题,引入并结合高光谱图像两种典型的相关性特征,具体包括:基于马尔可夫随机场(MRF)模型,建立描述相邻像元空间相关特征的约束;通过复杂度映射技术,建立描述相邻波段谱间相关(光谱分段平滑)特征的约束;并将上述两种约束同时引入NMF解混目标函数中。实验结果表明,对于一般自然地物场景或人造地物场景,相对于分段平滑和稀疏约束的非负矩阵分解(PSNMFSC)、交互投影子梯度的非负矩阵分解(APSNMF)和最小体积约束的非负矩阵分解(MVCNMF)这3种代表性NMF解混参考算法,该算法可进一步提高高光谱解混精度;对于空间相关或谱间相关特征中某一种不显著的特殊场景,也具有更好的适应能力。通过将空间相关和谱间相关特征相结合,较全面地反映了高光谱数据与解混相关的重要特征,能够对绝大多数真实高光谱数据进行高精度解混,对高光谱解混及后续应用领域相关研究均具有参考价值。     

对流层映射函数对GPS基线解算质量的影响

介绍了对流层延迟映射函数的发展过程,分析了NMF模型、GMF模型和VMF1模型的特点,利用GAMIT软件使用实测数据比较了三种模型对基线解算质量的影响。