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Fusion of anomaly algorithm decision maps and spectrum features for detecting buried explosive Hazards in forward looking infrared imagery
2011 / IEEE / 978-1-4673-0216-6
This item was taken from the IEEE Conference ' Fusion of anomaly algorithm decision maps and spectrum features for detecting buried explosive Hazards in forward looking infrared imagery ' Remediation of the threat of explosive hazards is an extremely important goal. Such hazards are responsible for an unacceptable number of deaths and injuries to civilians as well as soldiers throughout the world. In this article, we put forth a new method for aggregating image space anomaly algorithm decisions across time (multi-look) as well as across disparate algorithms in Universal Transverse Mercator (UTM) space for forward looking vehicle mounted (FL) long-wave infrared (LWIR) imagery. We also explore the utility of fast Fourier transform (FFT) spectrum features, which were previously used for FL ground penetrating radar (FLGPR), on aggregated UTM anomaly algorithm decision (UTMAAD) maps. On a final note, we also discuss modifications to our pre-screener, an ensemble of trainable size contrast filters, for UTMAAD maps. Targets not detected at the moment are also not found by a human under visual inspection. Preliminary lane-based cross validation (CV) experiments are reported using field data measurements from a U.S. Army test site.
Anomaly Algorithm Decision Map
Buried Explosive Hazard Detection
Forward Looking Infrared Imagery
Explosive Hazard Threat Remediation
Image Space Anomaly Algorithm
Forward Looking Vehicle Mounted Lwir Imagery
Long-wave Infrared Imagery
Fast Fourier Transform
Fft Spectrum Feature
Fl Ground Penetrating Radar
Trainable Size Contrast Filter
Lane-based Cross Validation
Us Army Test Site
Long Wave Infrared
Buried Explosive Hazards
Fast Fourier Transforms
Buried Object Detection
Universal Transverse Mercator Space