Proc. SPIE. 8357, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XVII
KEYWORDS: Target detection, Radar, Detection and tracking algorithms, Image processing, Image registration, Chemical elements, Global Positioning System, Ground penetrating radar, General packet radio service, Correlation function
The intent of this research is to align and compare an array of GPR (Ground Penetrating Radar) data
with historical data taken over similar pathways, separated in time, with soft positioning accuracies.
The objective is to develop an overlap of the two or more data runs to reduce the false alarm load on
the operator and automatically reveal new alarms showing up in the new data (change detection).
Data is taken with a GPR system. GPS (Global Positioning System) coordinates are stamped on the
data but are very inaccurate, therein rests the registration problem.
Two approaches have been taken to align the data sets:
1) Alarm registration through 2D correlation methods.
2) Image registration of ground contours using 2D correlation methods.
Radar data are displayed and analyzed within the context of the above algorithms. Data displays are
shown in 2D formats, with alarm registration displaying cross track vs. down track and ground contour
registration displaying down track vs. ground depth.
Preliminary results indicate a positive benefit from these registration processes, including:
Rapid detection of new alarms.
Reduction of the overall FAR (False Alarm Rate) load on the operator.
These processes have application to the support of radar systems in operational scenarios by decreasing
the load on operators and producing a more rapid ROA (Rate of Advance).
Proc. SPIE. 8017, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XVI
KEYWORDS: Target detection, Image processing algorithms and systems, Detection and tracking algorithms, Sensors, Image segmentation, Image processing, Image filtering, Palladium, Land mines, General packet radio service
A method for segmenting deformable shapes of soil and ground layers in successive GPR image frames is described in
this paper. First, pre-processing operators are applied to enhance the quality of each image frame. Second, local
histogram features are used to initialize membership probabilities of each pixel in the current image frame. Then, a
segmentation algorithm based on relaxation labeling is applied to perform image segmentation. This algorithm uses
information from previous and current image frames to perform layer identification by formulating the segmentation task
as a probabilistic relaxation labeling process in which the current frame image is used for initializing pixel membership
probabilities estimated from gray-level histograms. The previous frame image is used for estimating the compatibility
values to be utilized for segmenting the current frame image using mutual information among neighboring pixels. By
iteratively refining the membership probabilities of each pixel in the current image frame in parallel, an enhanced
segmentation is produced according to the refined probabilities. A distinguishing characteristic of this process is the
ability to incorporate both temporal contexts (down-track history information encoded as compatibilities) and spatial
contexts (current-scan pixel neighborhood information encoded as probabilities), concurrently. The segmented image is
post-processed by further filtering operations and checking for highly unlikely decisions to produce the final
Hybrid ground penetrating radar (GPR) and electromagnetic induction (EMI) sensors have advanced landmine detection
far beyond the capabilities of a single sensing modality. Both probability of detection (PD) and false alarm rate (FAR)
are impacted by the algorithms utilized by each sensing mode and the manner in which the information is fused.
Algorithm development and fusion will be discussed, with an aim at achieving a threshold probability of detection (PD)
of 0.98 with a low false alarm rate (FAR) of less than 1 false alarm per 2 square meters. Stochastic evaluation of prescreeners
and classifiers is presented with subdivisions determined based on mine type, metal content, and depth.
Training and testing of an optimal prescreener on lanes that contain mostly low metal anti-personnel mines is presented.
Several fusion operators for pre-screeners and classifiers, including confidence map multiplication, will be investigated
and discussed for integration into the algorithm architecture.
NIITEK (Non-Intrusive Inspection Technology, Inc) develops and fields vehicle-mounted mine and buried threat
detection systems. Since 2003, the NIITEK has developed and tested a remote robot-mounted mine detection
system for use in the NVESD AMDS program. This paper will discuss the road map of development since the
outset of the program, including transition from a data collection platform towards a militarized field-ready system
for immediate use as a remote countermine and buried threat detection solution with real-time autonomous threat
classification. The detection system payload has been integrated on both the iRobot Packbot and the Foster-Miller
Talon robot. This brief will discuss the requirements for a successful near-term system, the progressive
development of the system, our current real-time capabilities, and our planned upgrades for moving into and
supporting field testing, evaluation, and ongoing operation.