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A novel approach is proposed for combining multiple anomaly algorithm decisions with image space cell-structured features in a long wave infrared (LWIR) system in the context of forward looking (FL) buried explosive hazard detection along a road. A pre-screener is applied first, which is an ensemble of trainable size-contrast filters with mean shift clustering in Universal Transverse Mercator (UTM) space. Next, features from image chips representing anomaly decisions from different algorithms are extracted from UTM confidence maps based on maximally stable extremal regions (MSERs) and Gaussian mixture models (GMMs). Pre-screener hits in UTM space are back-projected into the video at multiple standoff distances and cell-structured local binary patterns (LBPs), histogram of gradients (HOGs) and mean-variance descriptors are extracted. Experiments are conducted using buried materials with varying metal contents and depths at a U.S. Army test site. Results are extremely encouraging for FL imaging and show a significant decrease in the number of false alarms (FAs). Targets not currently detected by our system are also not detected by a human under manual visual inspection.