An automatic detection system for buried explosive hazards in FL-LWIR and FL-GPR data

Abstract

Improvements to an automatic detection system for locating buried explosive hazards in forward-looking longwave infrared (FL-LWIR) imagery, as well as the system's application to detection in confidence maps and forwardlooking ground penetrating radar (FL-GPR) data, are discussed. The detection system, described in previous work, utilizes an ensemble of trainable size-contrast filters and the mean-shift algorithm in Universal Transverse Mercator (UTM) coordinates. Improvements of the raw detection algorithm include weighted mean-shift within the individual size-contrast filters and a secondary classification step which exacts cell structured image space features, including local binary patterns (LBP), histogram of oriented gradients (HOG), edge histogram descriptor (EHD), and maximally stable extremal regions (MSER) segmentation based shape information, from one or more looks and classifies the resulting feature vector using a support vector machine (SVM). FL-LWIR specific improvements include elimination of the need for multiple models due to diurnal temperature variation. The improved algorithm is assessed on FL-LWIR and FL-GPR data from recent collections at a US Army test site.