Sparsity inspired automatic target recognition

Vishal M. Patel, Nasser M. Nasrabadi, Rama Chellappa

Research output: Chapter in Book/Report/Conference proceedingConference contribution

12 Scopus citations

Abstract

In this paper, we develop a framework for using only the needed data for automatic target recognition (ATR) algorithms using the recently developed theory of sparse representations and compressive sensing (CS). We show how sparsity can be helpful for efficient utilization of data, with the possibility of developing real-time, robust target classification. We verify the efficacy of the proposed algorithm in terms of the recognition rate on the well known Comanche forward-looking infrared (FLIR) data set consisting of ten different military targets at different orientations.

Original languageEnglish (US)
Title of host publicationAutomatic Target Recognition XX; Acquisition, Tracking, Pointing, and Laser Systems Technologies XXIV; and Optical Pattern Recognition XXI
DOIs
StatePublished - 2010
Externally publishedYes
EventAutomatic Target Recognition XX; Acquisition, Tracking, Pointing, and Laser Systems Technologies XXIV; and Optical Pattern Recognition XXI - Orlando, FL, United States
Duration: Apr 5 2010Apr 8 2010

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume7696
ISSN (Print)0277-786X

Other

OtherAutomatic Target Recognition XX; Acquisition, Tracking, Pointing, and Laser Systems Technologies XXIV; and Optical Pattern Recognition XXI
Country/TerritoryUnited States
CityOrlando, FL
Period4/5/104/8/10

All Science Journal Classification (ASJC) codes

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering

Keywords

  • Automatic target recognition
  • Compressed sensing
  • Forward-looking infrared (FLIR) imagery
  • Sparse representation

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