EDS Image Classification by Projection after Partitioning

Abstract

In the analysis of multispectral x-ray dispersive images of geological materials, it is found that the large number of mineral phases typically present, leads to a large number of significant principal component images. This large dimensionality produces difficulties in graphically presenting the data, and interactively partitioning die image. A further problem is in the nature of the mineral phase distributions. Some phases will be well separated, in at least one projection direction, and other phases are continuous mixtures, with two or more end points. Both types of mineral phase distributions can be found intimately mixed in a geological sample. The very different types of these distributions implies that routines for searching n-space for clustering will have difficulty producing a good projection of the continuous mixture phase. Also for this reason it is difficult to see how alternative projection techniques can give a meaningful weight to a mixed phase in the presence of well separated components.Here we describe a multi-step image analysis which circumvents the above problem, and promises to be a convenient, and relatively simple extension of principal component analysis (PCA). The analysis method relies on the fact that there is very often, some clear separation of an image after PCA, and that this clear separation can be readily observed in the first few principal component images. Thus the information in the first few principal component scatter diagrams can be partitioned interactively into regions that are well separated from each other, but may be individually complex. PCA can then be applied to the data subsets, and the process can be repeated until a meaningful projection of the data can be viewed in a low dimensional scatter diagram. The real image can then be formed using either density or false color imaging, depending on which is appropriate for the subset alone. The method is thus finding projections after partitioning.