Processing forward-looking data for anomaly detection: Single-look, multi-look, and spatial classification

Many effective buried threat detection systems rely on close proximity and near vertical deployment over subsurface objects before reasonable performance can be obtained. A forward-looking sensor configuration, where an object can be detected from much greater distances, allows for safer detection of buried explosive threats, and increased rates of advance. Forward-looking configurations also provide an additional advantage of yielding multiple perspectives and looks at each subsurface area, and data from these multiple pose angles can be potentially exploited for improved detection. This work investigates several aspects of detection algorithms that can be applied to forward-looking imagery. Previous forward-looking detection algorithms have employed several anomaly detection algorithms, such as the RX algorithm. In this work the performance of the RX algorithm is compared to a scale-space approach based on Laplcaian of Gaussian filtering. This work also investigates methods to combine the detection output from successive frames to aid detection performance. This is done by exploiting the spatial colocation of detection alarms after they are mapped from image coordinates into world coordinates. The performance of the resulting algorithms are measured on data from a forward-looking vehicle mounted optical sensor system collected over several lanes at a western U.S. test facility. Results indicate that exploiting the spatial colocation of detections made in successive frames can yield improved performance.