Buried explosive hazard detection using forward-looking long-wave infrared imagery

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.