Discrimination of Objects Within Polarimetric Using Principal Component and Cluster Analysis

Abstract

We apply two multivariate image analysis techniques to several sets of spatially coincident, long-wave infrared (LWIR), polarimetric images to improve target contrast and/or aid in the identification of certain object features not present in the original image set. Principal component analysis (PCA) was used to obtain new representations of the scene that highlight target features based upon the variance of the variables used in the analysis. We show a representation of the scene that maintains the same level of target-to-background contrast (when compared to the conventional thermal and degree of linear polarization images), as well as additional information content contained in the resultant PCA analysis. Cluster analysis (CA) was used to group pixels of the image that have similar values for the variables chosen. We show that this method is an effective means for separating objects of interest from complex backgrounds, as well as subdividing different features of an object.

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Document Details

Document Type
Technical Report
Publication Date
Aug 01, 2007
Accession Number
ADA471003

Entities

People

  • Adrienne Raglin
  • David Ligon
  • Kristan P. Gurton
  • Melvin Felton

Organizations

  • United States Army Research Laboratory

Tags

Communities of Interest

  • Weapons Technologies

DTIC Thesaurus Topics

  • Algorithms
  • Contrast
  • Data Science
  • Data Sets
  • Discrimination
  • Factor Analysis
  • Identification
  • Image Processing
  • Information Science
  • Linear Polarization
  • Long-Wavelength Infrared Radiation
  • Polarization
  • Polarizers
  • Radiation
  • Rocket Launchers
  • Statistics
  • Target Recognition

Readers

  • Image Processing and Computer Vision.
  • Regression Analysis.
  • Vision Science/Vision Psychology/Cognitive Neuroscience.