Investigation on Constrained Matrix Factorization for Hyperspectral Image Analysis

Abstract

Matrix factorization is applied to unsupervised linear unmixing for hyperspectral imagery. The algorithm, called nonnegative matrix factorization, is used. It imposes a constraint on the non-negativity of the amplitudes of the recovered endmember spectral signatures as well as their fractional abundances. This ensures the recovery of physically meaningful endmembers and their abundances. This algorithm is further modified to include the sum-to-one constraint such that the sum of the fractional abundances for each pixel is unity. Several practical implementation issues in hyperspectral image unmixng are discussed. Some preliminary results from AVIRIS experiments are presented. We also discuss the advantages and possible limitations of this method in hyperspectral image analysis.

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Document Details

Document Type
Technical Report
Publication Date
Jul 25, 2005
Accession Number
ADA452786

Entities

People

  • Harold Szu
  • Ivica Kopriva
  • Qian Du

Organizations

  • Mississippi State University

Tags

Communities of Interest

  • Biomedical
  • Sensors

DTIC Thesaurus Topics

  • Algorithms
  • Anomaly Detection
  • Change Detection
  • Chemical Shifts
  • Computational Science
  • Computer Simulations
  • Detection
  • Detectors
  • Diagnostic Imaging
  • Earth Sciences
  • Factor Analysis
  • Hyperspectral Imagery
  • Information Processing
  • Magnetic Resonance
  • Materials
  • Maximum Likelihood Estimation
  • Remote Sensing

Fields of Study

  • Physics

Readers

  • Image Processing and Computer Vision.
  • Linear Algebra
  • Operations Research