Using Multiple Robust Parameter Design Techniques to Improve Hyperspectral Anomaly Detection Algorithm Performance

Abstract

Detecting and identifying objects of interest is the goal of all remote sensing. New advances, specifically in hyperspectral imaging technology have provided the analyst with immense amounts of data requiring evaluation. Several filtering techniques or anomaly detection algorithms have been proposed. However, most new algorithms are insufficiently verified to be robust to the broad range of hyperspectral data being made available. One such algorithm, AutoGAD, is tested here via two separate robust parameter design techniques to determine optimal parameters for consistent performance on a range of data with large attribute variances. Additionally, the results of the two techniques are compared for overall effectiveness. The results of the test as well as optimal parameters for AutoGAD are presented and future research efforts proposed.

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Document Details

Document Type
Technical Report
Publication Date
Mar 01, 2009
Accession Number
ADA500322

Entities

People

  • Matthew J. Davis

Organizations

  • Air Force Institute of Technology

Tags

Communities of Interest

  • Energy and Power Technologies
  • Human Systems
  • Space
  • Weapons Technologies

DTIC Thesaurus Topics

  • Air Force
  • Aircrafts
  • Computational Science
  • Data Science
  • Department Of Defense
  • Detection
  • Detectors
  • Dimensionality Reduction
  • Experimental Design
  • Hyperspectral Imagery
  • Improvised Explosive Devices
  • Information Processing
  • Information Science
  • Knowledge Management
  • Test And Evaluation
  • Two Dimensional
  • Unmanned Aerial Vehicles

Fields of Study

  • Computer science

Readers

  • Image Processing and Computer Vision.
  • Systems Analysis and Design