IRIT/INP-ENSEEIHT, University of Toulouse, 2 Rue Camichel, 31071 Toulouse Cedex 7, France.
Ultramicroscopy. 2012 Sep;120:25-34. doi: 10.1016/j.ultramic.2012.05.006. Epub 2012 Jun 1.
Recent advances in detectors and computer science have enabled the acquisition and the processing of multidimensional datasets, in particular in the field of spectral imaging. Benefiting from these new developments, Earth scientists try to recover the reflectance spectra of macroscopic materials (e.g., water, grass, mineral types…) present in an observed scene and to estimate their respective proportions in each mixed pixel of the acquired image. This task is usually referred to as spectral mixture analysis or spectral unmixing (SU). SU aims at decomposing the measured pixel spectrum into a collection of constituent spectra, called endmembers, and a set of corresponding fractions (abundances) that indicate the proportion of each endmember present in the pixel. Similarly, when processing spectrum-images, microscopists usually try to map elemental, physical and chemical state information of a given material. This paper reports how a SU algorithm dedicated to remote sensing hyperspectral images can be successfully applied to analyze spectrum-image resulting from electron energy-loss spectroscopy (EELS). SU generally overcomes standard limitations inherent to other multivariate statistical analysis methods, such as principal component analysis (PCA) or independent component analysis (ICA), that have been previously used to analyze EELS maps. Indeed, ICA and PCA may perform poorly for linear spectral mixture analysis due to the strong dependence between the abundances of the different materials. One example is presented here to demonstrate the potential of this technique for EELS analysis.
近年来，探测器和计算机科学的进步使得多维数据集的获取和处理成为可能，特别是在光谱成像领域。受益于这些新的发展，地球科学家试图恢复观测场景中宏观物质（例如水、草、矿物类型等）的反射光谱，并估计它们在获取图像的每个混合像素中的比例。这项任务通常被称为光谱混合分析或光谱解混（SU）。SU 的目的是将测量的像素光谱分解为一组组成光谱，称为端元，以及一组相应的分数（丰度），表示像素中每个端元的比例。类似地，在处理光谱图像时，显微镜通常试图绘制给定材料的元素、物理和化学状态信息。本文报告了如何成功地将专门用于遥感高光谱图像的 SU 算法应用于分析电子能量损失光谱（EELS）产生的光谱图像。SU 通常克服了其他多元统计分析方法（如主成分分析（PCA）或独立成分分析（ICA））固有的标准限制，这些方法以前曾用于分析 EELS 图谱。事实上，由于不同材料之间丰度的强烈相关性，ICA 和 PCA 可能在进行线性光谱混合分析时表现不佳。这里提供了一个示例来说明该技术在 EELS 分析中的潜力。
