A Taxonomy of Spectral Unmixing Algorithms and Performance Comparisons

Abstract

In this report, algorithms for spectral umnixing are organized into taxonomies and their performance is then compared. Our motivation is to collectively organize and relate algorithms in order to assess the current state-of-the-art in the field and to facilitate objective comparisons between methods. The hyperspectral sensing community is populated by investigators with disparate scientific backgrounds and efforts in spectral umnixing developed within disparate communities have inevitably led to duplication. This report is intended to remove ambiguity and redundancy by using a standard vocabulary, and clearly summarize what has and has not been done. As will be evident, the framework for the taxonomies derives its organization from the fundamental, philosophical assumptions imposed on the problem, rather than the common calculations they perform, or the similar outputs they might yield. The taxonomies are supplemented by a comparison of umnixing performance using techniques that typifY the approaches of wide classes of algorithms.

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Document Details

Document Type
Technical Report
Publication Date
Jan 15, 2002
Accession Number
ADA398413

Entities

People

  • N. Keshava

Organizations

  • Massachusetts Institute of Technology

Tags

Communities of Interest

  • Energy and Power Technologies
  • Sensors

DTIC Thesaurus Topics

  • Abstracts
  • Algorithms
  • Bayesian Networks
  • Convex Sets
  • Dimensionality Reduction
  • Distribution Functions
  • Estimators
  • Gaussian Distributions
  • Identification Systems
  • Order Statistics
  • Probability
  • Probability Distributions
  • Scattering
  • Standards
  • Statistical Algorithms
  • Statistics
  • Taxonomy

Readers

  • Image Processing and Computer Vision.
  • Instructional Design and Training Evaluation.
  • Systems Analysis and Design