Mine Classification With Imbalanced Data

David P. Williams

doi:10.1109/LGRS.2009.2021964

2009-07-01

PRE

David P. Williams *

Vincent Myers

Miranda Schatten Silvious

DOI：10.1109/LGRS.2009.2021964

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摘要

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In many remote-sensing classification problems, the number of targets (e.g., mines) present is very small compared with the number of clutter objects. Traditional classification approaches usually ignore this class imbalance, causing performance to suffer accordingly. In contrast, the recently developed infinitely imbalanced logistic regression (IILR) algorithm explicitly addresses class imbalance in its formulation. We describe this algorithm and give the details necessary to employ it for remote-sensing data sets that are characterized by class imbalance. The method is applied to the problem of mine classification on three real measured data sets. Specifically, classification performance using the IILR algorithm is shown to exceed that of a standard logistic regression approach on two land-mine data sets collected with a ground-penetrating radar and on one underwater-mine data set collected with a sidescan sonar.

Keyword:

Classification

imbalanced data

land mines

logistic regression (LR)

mine detection

radar

sonar

underwater mines