A Comparison Study of Dimension Estimation Algorithms

Abstract

The inherent dimension of hyperspectral data is commonly estimated for the purpose of dimension reduction. However, the dimension estimate itself may be a useful measure for extracting information about hyperspectral data, including scene content, complexity, and clutter. There are many ways to estimate the inherent dimension of data, each measuring the data in a different way. This paper compares a group of dimension estimation metrics on a variety of data, both full scene and individual material regions, to determine the relationship between the different estimates and what features each method is measuring when applied to complex data.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 2010
Accession Number
AD1108575

Entities

People

  • Ariel Schlamm
  • David W. Messinger
  • Ronald G. Resmini
  • William Basener

Organizations

  • George Mason University
  • MITRE Corporation
  • Rochester Institute of Technology

Tags

Communities of Interest

  • Sensors

DTIC Thesaurus Topics

  • Algorithms
  • Anomaly Detection
  • Change Detection
  • Corporations
  • Data Analysis
  • Data Processing
  • Data Set
  • Data Sets
  • Detection
  • Detectors
  • Digital Data
  • Dimensionality Reduction
  • Eigenvalues
  • Geometry
  • Hyperspectral Imagery
  • Materials
  • New York
  • Remote Sensing
  • Schools
  • Space Based
  • Target Detection
  • Two Dimensional
  • Universities

Readers

  • Computer Vision.