False Alarm Mitigation and Feature based Discrimination for Airborne Mine Detection

Abstract

The aim of an anomaly detector is to locate spatial target locations that show significantly different spectral/spatial characteristics as compared to the background. Typical anomaly detectors can achieve a high probability of detection, however at the cost of significantly high false alarm rates. for successful minefield detection there is a need for a further processing step to identify mine-like targets and/or reject non-mine targets in order to improve the mine detection to false alarm ratio. in this paper, we discuss a number of false alarm mitigation (FAM) modalities for MWIR imagery. in particular, we investigate measures based on circularity, gray scale shape profile and reflection symmetry. the performance of these modalities is evaluated for false alarm mitigation using real airborne MWIR data at different times of the day and for different spectral bands. We also motivate a feature based clustering and discrimination scheme based on these modalities to classify similar targets. While false alarm mitigation is primarily used to reject non-mine like targets, feature based clustering can be used to select similar-looking mine-like targets. Minefield detection can subsequently proceed on each localized cluster of similar looking targets.

Department(s)

Electrical and Computer Engineering

Keywords and Phrases

Airborne minefield detection; False alarm mitigation; Feature based clustering; Mine detection

International Standard Serial Number (ISSN)

0277-786X

Document Type

Article - Conference proceedings

Document Version

Final Version

File Type

text

Language(s)

English

Rights

© 2024 Society of Photo-optical Instrumentation Engineers, All rights reserved.

Publication Date

20 Dec 2004

Link to Full Text

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