Statistical Classification on a Multispectral Image Susan Kolakowski Kristin Strackerjan Donald Taylor Seth Weith-Glushko 0307-834 Multivariate Statistics for Imaging Science May 12, 2005

Outline Introduction Background Procedure Results/Analysis Conclusion Questions I will give you a brief introduction and then Kristin will give some background. Seth will talk about the procedure. Don will talk about the results/analysis. I will close with the conclusion and then we will allow some time for questions.

Introduction MISI Image Supervised Unsupervised Analysis Methods Gaussian Maximum Likelihood (GML) Unsupervised Minimum Distance to the Mean (MDM) Analysis Methods Confusion matrices, Jeffries-Matusita distances, transformed divergences

Background Why classify an image? Separate pixels into different classes based on their spectral information Human visual system does not distinguish between different classes of material outside of the visible spectrum (RGB vs VIS/NIR) Automate this process to remove as much bias as possible

Background Methods of Classification (1) Unsupervised Classification Little interference from the user Input parameters: Number of classes Threshold Number of iterations K-means vs. ISODATA

Background Methods of Classification (2) Supervised Classification Gaussian Maximum Likelihood (GML) classifier Use Regions of Interest (ROIs) defined in ENVI to train the GML classifier GML is calculated based on the probability of pixel X belonging to class i:

Background Separability of Classes In order to quantify the degree of overlap between potential classes, we used Transform Divergence and the Jeffries-Matusita (JM) distances Both of these methods allow us to see how separable the data is by comparing groups of three bands at a time

Background Transformed Divergence

Background Jeffries-Matusita (JM) Distance

Background Quality of the Classifier Confusion Matrices Dependent data set Independent data set Randomly selected data

Procedure Selection of test data Code generation for each algorithm in the IDL programming environment Run the classification algorithm and generate a classification map Calculate the best three-band combo for display using Jeffries-Matusita distance and transformed divergence Execute the unsupervised algorithm Select training data based off of unsupervised classification map

Original Image with Regions of Interest overlaid

Procedure Run the supervised classification algorithm Calculate quality metrics Visual comparison of the classification maps Confusion matrices Visual inspection of scatterplots generated with Jeffries-Matusita distance and transformed divergence

Unsupervised Color Class Contents Water Sand/Dirt Grass Trees Roads/Pavement Urban

Unsupervised – Classification Matrices

Unsupervised - Scatter Plots

Supervised

Supervised – Classification Matrices

Supervised - Scatter Plots

Conclusion Supervised vs. Unsupervised Advantages and Disadvantages MDM vs. GML

Questions??