Characterization and removal of unwanted artifacts in ground penetrating radar (GPR) imagery is a non-trivial task. The
many factors affecting the presence, magnitude, and duration of such artifacts include their origin (man-made or
naturally occurring), location (above ground or in-ground), dielectric constant, and moisture content, to name a few. It is
of significant benefit to anomaly detection systems to remove such artifacts to reduce false alarm rates and increase
threat alarm confidences. Man-made artifacts are typically a result of secondary reflections from the radar emitting
surface and its related hardware. These "self-signatures" are manifested as artifacts below the ground surface that tend to
be visible for all scans. However, when the sensor height is not held constant above the ground, the position (in time)
and magnitude of the reflections become variable and difficult to predict. Naturally occurring artifacts include ground
layers, sub-surface water layers, etc.
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.
Novel designs of skin friction and heat flux sensors have been developed based on advanced materials and processing techniques. These sensors produce dynamic, time-resolved, direct measurements of skin friction and heat flux, especially tailored towards turbulent flows. The skin friction sensors use ionic polymer transducers, which contain no moving parts, directly measure shear, and can be surface mounted with minimal flow intrusion. The sensors exhibit measurement accuracy in fluctuating shear on the order of 4.92% over a range of stresses of +/- 3 Pa and signal-to-noise-ratio on the order of 60 dB. The frequency response of the sensor is on the order of 10 kHz. An approach for automatic recalibration and error compensation based on changes of impedance has been developed. This process allows in-situ recalibration of the sensors under varying temperature conditions. The heat flux sensors are made with thin-film deposition which allows fine arrays to be created. The measured Seebeck coefficient (temperature sensitivity) of the deposited metals is 23.5 μV/<sup>o</sup>C, which closely matches that of bulk wire.