Accession Number:

ADA517700

Title:

Machine Learning Approach for Target Selection and Threat Classification of Wide Area Survey Data

Descriptive Note:

Final rept.

Corporate Author:

SCIENCE APPLICATIONS INTERNATIONAL CORP WASHINGTON DC

Report Date:

2007-12-01

Pagination or Media Count:

22.0

Abstract:

This project had its genesis in the FY-2007 SERDP Proposal Cycle as proposal 07 MM04-007. Following the sale of AETC to SAIC in November 2006, the project was awarded to SAIC by HECSA as Contract Number W912HQ-07-C-0023. The Project Plan calls for applying the techniques developed during the previous projects, UX1322 and UX1455 to the vehicular and airborne Wide Area MTADS surveys of western desert ranges. In project UX-1455 we demonstrated that using machine learning techniques inherent to the Feature Analyst software it is possible to autonomously identify, with high confidence and accuracy, nearly all of the UXO in a survey dataset. Furthermore, we showed the technology could significantly reduce the number of false positives using a two-pass workflow in Feature Analyst with the Target Picker and Target Ranker modules operating sequentially separately from each other.

Subject Categories:

  • Optical Detection and Detectors
  • Magnetic and Electric Field Detection and Detectors
  • Ammunition and Explosives

Distribution Statement:

APPROVED FOR PUBLIC RELEASE