SAR automatic target recognition via non-negative matrix approximations

Vahid Riasati, Umamahesh Srinivas, Vishal Monga

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

4 Scopus citations


The set of orthogonal eigen-vectors built via principal component analysis (PCA), while very effective for com- pression, can often lead to loss of crucial discriminative information in signals. In this work, we build a new basis set using synthetic aperture radar (SAR) target images via non-negative matrix approximations (NNMAs). Owing to the underlying physics, we expect a non-negative basis and an accompanying non-negative coecient set to be a more accurate generative model for SAR proles than the PCA basis which lacks direct physical interpretation. The NNMA basis vectors while not orthogonal capture discriminative local components of SAR target images. We test the merits of the NNMA basis representation for the problem of automatic target recognition using SAR images with a support vector machine (SVM) classier. 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 classication performance and availability of training.

Original languageEnglish (US)
Title of host publicationAutomatic Target Recognition XXII
StatePublished - 2012
EventAutomatic Target Recognition XXII - Baltimore, MD, United States
Duration: Apr 23 2012Apr 24 2012

Publication series

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


OtherAutomatic Target Recognition XXII
Country/TerritoryUnited States
CityBaltimore, MD

All Science Journal Classification (ASJC) codes

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


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