Paper
23 May 2011 Buried explosive hazard detection using forward-looking long-wave infrared imagery
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Abstract
Trainable size-contrast filters, similar to local dual-window RX anomaly detectors, utilizing the Bhattacharyya distance are used to detect buried explosive hazards in forward-looking long-wave infrared imagery. The imagery, captured from a moving vehicle, is geo-referenced, allowing projection of pixel coordinates into (UTM) Universal Transverse Mercator coordinates. Size-contrast filter detections for a particular frame are projected into UTM coordinates, and peaks are detected in the resulting density using the mean-shift algorithm. All peaks without a minimum number of detections in their local neighborhood are discarded. Peaks from individual frames are then combined into a single set of tentative hit locations, and the same mean-shift procedure is run on the resulting density. Peaks without a minimum number of hit locations in their local neighborhood are removed. The remaining peaks are declared as target locations. The mean-shift steps utilize both the spatial and temporal information in the imagery. Scoring is performed using ground truth locations in UTM coordinates. The size-contrast filter and mean-shift parameters are learned using a genetic algorithm which minimizes a multiobjective fitness function involving detection rate and false alarm rate. Performance of the proposed algorithm is evaluated on multiple lanes from a recent collection at a US Army test site.
© (2011) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
K. Stone, J. M. Keller, M. Popescu, and C. J. Spain "Buried explosive hazard detection using forward-looking long-wave infrared imagery", Proc. SPIE 8017, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XVI, 801725 (23 May 2011); https://doi.org/10.1117/12.886673
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Cited by 18 scholarly publications.
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KEYWORDS
Sensors

Target detection

Long wavelength infrared

Metals

Detection and tracking algorithms

Explosives

Roads

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