Substantial interest resides in identifying sensors, algorithms and fusion theories to detect explosive hazards. This is a significant research effort because it impacts the safety and lives of civilians and soldiers alike. However, a challenging aspect of this field is we are not in conflict with the threats (objects) per se. Instead, we are dealing with people and their changing strategies and preferred method of delivery. Herein, we investigate one method of threat delivery, side attack explosive ballistics (SAEB). In particular, we explore a vehicle-mounted synthetic aperture acoustic (SAA) platform. First, a wide band SAA signal is decomposed into a higher spectral resolution signal. Next, different multi/hyperspectral signal processing techniques are explored for manual band analysis and selection. Last, a convolutional neural network (CNN) is used for filter learning and classification relative to the full signal versus different subbands. Performance is assessed in the context of receiver operating characteristic (ROC) curves on data from a U.S. Army test site that contains multiple target and clutter types, levels of concealment and times of day. Preliminary results indicate that a machine learned CNN solution can achieve better performance than our previously established human engineered Fourier-based Fraz feature with kernel support vector machine classification.
A serious threat to civilians and soldiers is buried and above ground explosive hazards. The automatic detection of such
threats is highly desired. Many methods exist for explosive hazard detection, e.g., hand-held based sensors, downward
and forward looking vehicle mounted platforms, etc. In addition, multiple sensors are used to tackle this extreme problem,
such as radar and infrared (IR) imagery. In this article, we explore the utility of feature and decision level fusion of learned
features for forward looking explosive hazard detection in IR imagery. Specifically, we investigate different ways to fuse
learned iECO features pre and post multiple kernel (MK) support vector machine (SVM) based classification. Three MK
strategies are explored; fixed rule, heuristics and optimization-based. Performance is assessed in the context of receiver
operating characteristic (ROC) curves on data from a U.S. Army test site that contains multiple target and clutter types,
burial depths and times of day. Specifically, the results reveal two interesting things. First, the different MK strategies
appear to indicate that the different iECO individuals are all more-or-less important and there is not a dominant feature.
This is reinforcing as our hypothesis was that iECO provides different ways to approach target detection. Last, we observe
that while optimization-based MK is mathematically appealing, i.e., it connects the learning of the fusion to the underlying
classification problem we are trying to solve, it appears to be highly susceptible to over fitting and simpler, e.g., fixed rule
and heuristics approaches help us realize more generalizable iECO solutions.