Local rank analysis for exploratory spectroscopic image analysis. Fixed Size Image Window-Evolving Factor Analysis

Methods used for this purpose should be local and be able to take into account the complex spatial structure of the image. Fixed Size Moving Window-Evolving Factor Analysis has been a powerful approach to locally define the complexity of a process through the subsequent PCA analyses of data subsets built by moving a fixed size window along a unique process direction (e.g., time, pH). Spectroscopic images have two or three spatial directions (in surface or multilayer images, respectively). Algorithms based on local data analysis should be adapted to preserve this higher dimensionality in order to provide a representative description of the image complexity. Fixed Size Image Window-Evolving Factor Analysis (FSIW-EFA) modifies the parent local rank algorithm to achieve this purpose.