干旱区生态环境知识资源中心

In previous work an automatic detection system for locating buried explosive hazards in forward-looking long-wave infrared (FL-LWIR) and forward-looking ground penetrating radar (FL-GPR) data was presented. This system consists of an ensemble of trainable size-contrast filters prescreener coupled with a secondary classification step which extracts cell-structured image space features, such as local binary patterns (LBP), histogram of oriented gradients (HOG), and edge histogram descriptors (EHD), from multiple looks and classifies the resulting feature vectors using a support vector machine. Previously, this system performed image space to UTM coordinate mapping under a flat earth assumption. This limited its applicability to flat terrain and short standoff distances. This paper demonstrates a technique for dense 3D scene reconstruction from a single vehicle mounted FL-LWIR camera. This technique utilizes multiple views and standard stereo vision algorithms such as polar rectification and optimal correction. Results for the detection algorithm using this 3D scene reconstruction approach on data from recent collections at an arid US Army test site are presented. These results are compared to those obtained under the flat earth assumption, with special focus on rougher terrain and longer standoff distance than in previous experiments. The most recent collection also allowed comparison between uncooled and cooled FL-LWIR cameras for buried explosive hazard detection.