Multivariate Spectral Analysis to Extract Materials from Multispectral Data

Abstract

This effort investigates various multivariate analysis techniques for classification/identification to extract natural and manmade features reliably from broad-band spectral imaging data/multispectral imagery. An enhanced Bayesian method is proposed and is demonstrated to exhibit increased accuracy over three conventional supervised classifiers. Broad-band spectral properties of various materials are examined and the perturbations on spectra of pure materials introduced by mixtures are shown. A mixing model that uses multiple linear regression constrained by two physical properties is tested.

Open PDF

Document Details

Document Type
Technical Report
Publication Date
Sep 01, 1993
Accession Number
ADA274142

Entities

People

  • Donald A. Davis
  • Robert S. Rand

Organizations

  • Army Geospatial Center

Tags

Communities of Interest

  • Human Systems
  • Space

DTIC Thesaurus Topics

  • Algorithms
  • Analysis Of Variance
  • Artificial Intelligence
  • Construction
  • Data Mining
  • Data Science
  • Data Sets
  • Databases
  • Engineers
  • Grasses
  • Information Processing
  • Information Science
  • Jet Propulsion
  • New York
  • Pattern Recognition
  • Photography
  • Statistical Analysis

Readers

  • Acoustical Oceanography.
  • Computer Vision.
  • Neural Network Machine Learning.

Technology Areas

  • AI & ML