Introduction to Hyperspectral/Multivariate Image Analysis

Course Description

Intro to Hyperspectral/Multivariate Image Analysis (MIA) is designed to give the student practical experience. Before the course, students will be sent a precourse reading assignment covering some of the basic background and principles of MIA. The course will start with a brief review of principal components analysis (PCA) and partial least squares (PLS) regression and how they are used in image analysis. Additional topics to be covered included multivariate image regression, and preprocessing to capture textural information. Methods to mitigate the effects of background interference, e.g. clutter, will also be discussed. 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 Chemometricians, Linear Algebra for Chemometricians, and Chemometrics I--PCA, or equivalent experience.

Course Outline

1. Intro to 3-way arrays
   Objects and Variables
   Example Applications
   Structure of Multivariate Images
   Comparison to other sources of 3-way data
   
2. Practical Multivariate Image Analysis (MIA)
   Review of Principal Components Analysis
   Scores, loadings and projections
   Unusual samples, residuals and T^2
   Matricizing of images
   Scores images, loadings
   Overlays
   Score/score plots: density
   Links between scores space and the image plane
   Contrast enhancement
   Image SIMCA
   
3. Multivariate Image Regression analysis (MIR)
   Review of regression: MLR/PCR/PLS
   Scores, loadings
   Image plane and score linking
   Cross validation for images
   
4. Preprocessing
   Centering and scaling
   Smoothing and derivitizing
   Scatter correction
   
5. Intro to texture analysis
   Finite Fourier Transform (FFT)
   SVD Spectrum
   Angle Measurement Technique (AMT)
   Kriging
   MIR using texture transforms
   
6. Alternatives to PCA/PLS
   Multivariate Curve Resolution
   PARAFAC on series of images
   Classical Least Squares
   Positive Matrix Factorization
   Generalized Least Squares and decluttering
   Extended addition model
   Evolving Window Factor Analysis
   Target Factor Analysis