Joint sparse representation based automatic target recognition in SAR images

Haichao Zhang, Nasser M. Nasrabadi, Thomas S. Huang, Yanning Zhangt

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

Abstract

In this paper, we introduce a novel joint sparse representation based automatic target recognition (ATR) method using multiple views, which can not only handle multi-view ATR without knowing the pose but also has the advantage of exploiting the correlations among the multiple views for a single joint recognition decision. We cast the problem as a multi-variate regression model and recover the sparse representations for the multiple views simultaneously. The recognition is accomplished via classifying the target to the class which gives the minimum total reconstruction error accumulated across all the views. Extensive experiments have been carried out on Moving and Stationary Target Acquisition and Recognition (MSTAR) public database to evaluate the proposed method compared with several state-of-the-art methods such as linear Support Vector Machine (SVM), kernel SVM as well as a sparse representation based classifier. Experimental results demonstrate that the effectiveness as well as robustness of the proposed joint sparse representation ATR method.

Original languageEnglish (US)
Title of host publicationAlgorithms for Synthetic Aperture Radar Imagery XVIII
DOIs
StatePublished - Jun 29 2011
EventAlgorithms for Synthetic Aperture Radar Imagery XVIII - Orlando, FL, United States
Duration: Apr 27 2011Apr 28 2011

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume8051
ISSN (Print)0277-786X

Other

OtherAlgorithms for Synthetic Aperture Radar Imagery XVIII
Country/TerritoryUnited States
CityOrlando, FL
Period4/27/114/28/11

Keywords

  • Automatic target recognition
  • Joint sparse representation
  • MSTAR
  • SAR

ASJC Scopus subject areas

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

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