SLO blind data set inversion and classification using physically complete models

I. Shamatava; F. Shubitidze; J. P. Fernández; B. E. Barrowes; K. O'Neill; T. M. Grzegorczyk; A. Bijamov

doi:10.1117/12.850621

29 April 2010 SLO blind data set inversion and classification using physically complete models

I. Shamatava, F. Shubitidze, J. P. Fernández, B. E. Barrowes, K. O'Neill, T. M. Grzegorczyk, A. Bijamov

Proceedings Volume 7664, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XV; 766404 (2010) https://doi.org/10.1117/12.850621
Event: SPIE Defense, Security, and Sensing, 2010, Orlando, Florida, United States

Abstract

Discrimination studies carried out on TEMTADS and Metal Mapper blind data sets collected at the San Luis Obispo UXO site are presented. The data sets included four types of targets of interest: 2.36" rockets, 60-mm mortar shells, 81-mm projectiles, and 4.2" mortar items. The total parameterized normalized magnetic source (NSMS) amplitudes were used to discriminate TOI from metallic clutter and among the different hazardous UXO. First, in object's frame coordinate, the total NSMS were determined for each TOI along three orthogonal axes from the training data provided by the Strategic Environmental Research and Development Program (SERDP) along with the referred blind data sets. Then the inverted total NSMS were used to extract the time-decay classification features. Once our inversion and classification algorithms were tested on the calibration data sets then we applied the same procedure to all blind data sets. The combined NSMS and differential evolution algorithm is utilized for determine the NSMS strengths for each cell. The obtained total NSMS time-decay curves were used to extract the discrimination features and perform classification using the training data as reference. In addition, for cross validation, the inverted locations and orientations from NSMS-DE algorithm were compared against the inverted data that obtained via the magnetic field, vector and scalar potentials (HAP) method and the combined dipole and Gauss-Newton approach technique. We examined the entire time decay history of the total NSMS case-by-case for classification purposes. Also, we use different multi-class statistical classification algorithms for separating the dangerous objects from non hazardous items. The inverted targets were ranked by target ID and submitted to SERDP for independent scoring. The independent scoring results are presented.

Citation Download Citation

I. Shamatava, F. Shubitidze, J. P. Fernández, B. E. Barrowes, K. O'Neill, T. M. Grzegorczyk, and A. Bijamov "SLO blind data set inversion and classification using physically complete models", Proc. SPIE 7664, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XV, 766404 (29 April 2010); https://doi.org/10.1117/12.850621

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