Feature selection for physics model based object discrimination


Conference Paper

We investigated the application of two state-of-the-art feature selection algorithms for subsurface target discrimination. One is called joint classification and feature optimization (JCFO), which imposes a sparse prior on the features, and optimizes the classifier and its predictors simultaneously via an expectation maximization (EM) algorithm. The other selects features by directly maximizing the hypothesis margin between targets and clutter. The results of feature selection and target discrimination are demonstrated using wideband electromagnetic induction data measured at data collected at the Aberdeen Proving Ground Standardized Test Site for UXO discrimination. It is shown that the classification performance is significantly improved by only including a compact set of relevant features.

Full Text

Duke Authors

Cited Authors

  • Wang, C; Collins, L

Published Date

  • October 24, 2005

Published In

Volume / Issue

  • 5794 / PART II

Start / End Page

  • 1200 - 1208

International Standard Serial Number (ISSN)

  • 0277-786X

Digital Object Identifier (DOI)

  • 10.1117/12.603451

Citation Source

  • Scopus