Feature selection for physics model based object discrimination

Chunmei Wang; Leslie Collins

doi:10.1117/12.603451

10 June 2005 Feature selection for physics model based object discrimination

Chunmei Wang, Leslie Collins

Proceedings Volume 5794, Detection and Remediation Technologies for Mines and Minelike Targets X; (2005) https://doi.org/10.1117/12.603451
Event: Defense and Security, 2005, Orlando, Florida, United States

Abstract

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.

Citation Download Citation

Chunmei Wang and Leslie Collins "Feature selection for physics model based object discrimination", Proc. SPIE 5794, Detection and Remediation Technologies for Mines and Minelike Targets X, (10 June 2005); https://doi.org/10.1117/12.603451

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