Introduction to Hyperspectral/Multivariate Image Analysis

Course Description

Hyperspectral images are becoming increasingly common in analytical chemistry and remote sensing applications. They are based on several types of spectroscopy and spectrometry including Raman, IR and SIMS. Introduction to Hyperspectral/Multivariate Image Analysis (MIA) starts with a brief review of sources of multivariate images and tools for viewing and investigating them. Particle analysis is then considered, followed by texture analysis. Practical image analysis with Principal Components Analysis (PCA) demonstrates how information from hyperspectral images can be compressed and displayed, and how classification tools can be used to identify similar areas. The course concludes with some examples of Multivariate Curve Resolution (MCR) on images and demonstrates how it can be used to create chemical maps. The course includes hands-on computer time for participants to work example problems using PLS_Toolbox and MIA_Toolbox, or Solo+MIA.

Prerequisites

MATLAB for ChemometriciansLinear Algebra for Chemometricians, and Chemometrics I–PCA, or equivalent experience.

Course Outline

  1. Intro to 3-way Arrays
    1. Objects and variables
    2. Example applications
    3. Structure of multivariate images
    4. Comparison to other sources of 3-way data 
  2. Viewing Multivariate Images
    1. Selecting channels for RGB
    2. Viewing images at specific peaks
    3. Integrating peaks for noise reduction
  3. Particle Analysis
    1. Finding particles in images
    2. Calculating particle statistics
    3. Creating multivariate models on particles
  4. Texture Analysis
    1. Definition of texture
    2. Methods for creating texture spectra from images
    3. Using texture spectra in regression and classification
  5. Practical Multivariate Image Analysis (MIA)
    1. Review of Principal Components Analysis (PCA)
    2. Scores, loadings and projections
    3. Unusual samples, residuals and T^2
    4. Matricizing of images
    5. Scores images, loadings
    6. Score/score plots: density
    7. Links between scores space and the image plane
    8. Contrast enhancement, color maps
    9. Classification on images: SIMCA & PLS-DA
    10. Other methods, e.g. Maximal Autocorrelation Factors (MAF)
  6. Multivariate Curve Resolution (MCR) on Images
    1. Basics of MCR
    2. Creating chemical maps