Incorporation of operator knowledge for improved HMDS GPR classification

Levi Kennedy; Jessee R. McClelland; Joshua R. Walters

doi:10.1117/12.918481

10 May 2012 Incorporation of operator knowledge for improved HMDS GPR classification

Levi Kennedy, Jessee R. McClelland, Joshua R. Walters

Proceedings Volume 8357, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XVII; 835722 (2012) https://doi.org/10.1117/12.918481
Event: SPIE Defense, Security, and Sensing, 2012, Baltimore, Maryland, United States

Abstract

The Husky Mine Detection System (HMDS) detects and alerts operators to potential threats observed in groundpenetrating RADAR (GPR) data. In the current system architecture, the classifiers have been trained using available data from multiple training sites. Changes in target types, clutter types, and operational conditions may result in statistical differences between the training data and the testing data for the underlying features used by the classifier, potentially resulting in an increased false alarm rate or a lower probability of detection for the system. In the current mode of operation, the automated detection system alerts the human operator when a target-like object is detected. The operator then uses data visualization software, contextual information, and human intuition to decide whether the alarm presented is an actual target or a false alarm. When the statistics of the training data and the testing data are mismatched, the automated detection system can overwhelm the analyst with an excessive number of false alarms. This is evident in the performance of and the data collected from deployed systems. This work demonstrates that analyst feedback can be successfully used to re-train a classifier to account for variable testing data statistics not originally captured in the initial training data.

Citation Download Citation

Levi Kennedy, Jessee R. McClelland, and Joshua R. Walters "Incorporation of operator knowledge for improved HMDS GPR classification", Proc. SPIE 8357, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XVII, 835722 (10 May 2012); https://doi.org/10.1117/12.918481

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