A comparative study of basis selection techniques for automatic target recognition

Umamahesh Srinivas, Vishal Monga, Vahid Riasati

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

1 Scopus citations


Often in automatic target recognition (ATR) problems, a small number of representative features that encapsulate image information are usually extracted from the target images prior to the actual classification procedure. In literature, principal component analysis (PCA) is one of the most widely used feature extraction techniques. In this paper, we investigate the capability of basis representations to encode discriminative information for target classification using synthetic aperture radar (SAR) imagery. Specifically, we consider the two different scenarios of shared basis built using all available training and class-specific basis using training from each class separately. We compare the traditional PCA-based technique with basis representations constructed using oriented PCA and non-negative matrix approximations (NNMA). Experiments on the benchmark MSTAR database reveal the merits of basis selection techniques that can model imaging physics more closely and can capture inter-class variability, in addition to identifying a trade-off between classification performance and availability of training.

Original languageEnglish (US)
Title of host publication2012 IEEE Radar Conference
Subtitle of host publicationUbiquitous Radar, RADARCON 2012 - Conference Program
Number of pages4
StatePublished - Jul 30 2012
Event2012 IEEE Radar Conference: Ubiquitous Radar, RADARCON 2012 - Atlanta, GA, United States
Duration: May 7 2012May 11 2012

Publication series

NameIEEE National Radar Conference - Proceedings
ISSN (Print)1097-5659


Other2012 IEEE Radar Conference: Ubiquitous Radar, RADARCON 2012
Country/TerritoryUnited States
CityAtlanta, GA

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering


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