On the registration of FLGPR and IR data for a forward-looking landmine detection system and its use in eliminating FLGPR false alarms

Stone, Kevin; Keller, James M.; Ho, K. C.; Busch, M.; Gader, Paul D.

doi:10.1117/12.782238

Cited by 20 publications

(11 citation statements)

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“…We have shown in previous work that if forward-looking infrared (FLIR) or visible-spectrum imagery is combined with L-band FLGPR, false alarm rates can be reduced significantly. [10][11][12][13][14] We also demonstrated that fusing multiple sub-bands and spectral features in L-band FLGPR improves detection performance. [15][16][17] X -Band Radar L -Band Radar Since 2005, the U.S. Army Night Vision and Electronic Sensors Directorate (NVESD) has been integrating sensing technologies to aid the soldier in detecting buried targets while maintaining Situational Awareness (SA) in route clearance and convoy escort vehicles.…”

Section: Introductionmentioning

confidence: 82%

An apodization approach for processing forward-looking GPR for buried target detection

2015

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A forward-looking and -moving ground-penetrating radar (GPR) acquires data that can be used for buried target detection. As the platform moves forward the sensor can acquire and form a sequence of images for a common spatial region. Due to the near-field nature of relevant collection scenarios, the point-spread function (PSF) varies significantly as a function of the spatial position, both within the scene and relative to the sensor platform. This variability of the PSF causes computational difficulties for matched-filter and related processing of the full video sequence. One approach to circumventing this difficulty is to coherently or incoherently integrate the video frames, and then perform detection processing on the integrated image. Here, averaging over the space-and motion-variant nature of the PSFs for each frame causes the PSF for the integrated image to appear less space-variant. Another alternative-and the one we investigate in this paper-is to transform each image from the conventional (range, cross-range) coordinate system to a (range, sine-angle) coordinate system in which the PSF is approximated as spatially invariant. The advantage of the (range, sine-angle) coordinate space is that methods that require space-invariance can be directly applied. Here we develop a multi-anodization approach, which results in a significantly improved image. To evaluate the relative advantages of this procedure, we will empirically measure the integrated side-lobe ratio, which represents the reduction in the side-lobes before and after applying the algorithm.

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Section: Introductionmentioning

confidence: 82%

An apodization approach for processing forward-looking GPR for buried target detection

2015

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“…We have shown in previous work that if forward-looking infrared (FLIR) or visible-spectrum imagery is combined with L-band FLGPR, false alarm rates can be reduced significantly. [10][11][12][13][14] We also demonstrated that fusing multiple sub-bands and spectral features in L-band FLGPR improves detection performance. [15][16][17] Since 2005, the U.S. Army Night Vision and Electronic Sensors Directorate (NVESD) has been integrating sensing technologies to aid the soldier in detecting explosive hazards while maintaining Situational Awareness (SA) in route clearance and convoy escort vehicles.…”

Section: Introductionmentioning

confidence: 82%

“…We have shown in our previous research that fusing other sensors with FLGPR can significantly reduce the false alarm (FA) rate due to clutter, while maintaining a 90-100% probability of detection (p d ). [10][11][12][13][14][15][16][17]19 However, data and sensor fusion methods are only as good as the data and sensors to which they are applied. We aim to build upon our previous fusion work by proposing a method which uses both L-band and X-band FLGPR to improve detection statistics for standoff-detection of explosive hazards.…”

Section: Introductionmentioning

confidence: 99%

Multi-band sensor-fused explosive hazards detection in forward-looking ground penetrating radar

et al. 2014

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Explosive hazard detection and remediation is a pertinent area of interest for the U.S. Army. There are many types of detection methods that the Army has or is currently investigating, including ground-penetrating radar, thermal and visible spectrum cameras, acoustic arrays, laser vibrometers, etc. Since standoff range is an important characteristic for sensor performance, forward-looking ground-penetrating radar has been investigated for some time. Recently, the Army has begun testing a forward-looking system that combines L-band and X-band radar arrays. Our work focuses on developing imaging and detection methods for this sensor-fused system. In this paper, we investigate approaches that fuse L-band radar and X-band radar for explosive hazard detection and false alarm rejection. We use multiple kernel learning with support vector machines as the classification method and histogram of gradients (HOG) and local statistics as the main feature descriptors. We also perform preliminary testing on a context aware approach for detection. Results on government furnished data show that our false alarm rejection method improves area-under-ROC by up to 158%.

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“…The camera was fitted with a differential Global Positioning System with an inertial measurement unit (IMU) that was placed directly next to the camera in order to record the vehicle GPS and IMU information. Together this information was used to map image-space alarms into world-space coordinates using methods previously used for this data 20,21 .…”

Section: Forward-looking Infrared Camera Datamentioning

confidence: 99%

“…These cluster centers are then mapped from pixel coordinates into locations on the ground to which they correspond. Specifically, the pixel locations are mapped into Universal Transverse Mercator (UTM) coordinates using a well-established mapping technique applied in numerous FLIR studies 20,21 . Once in UTM coordinates, a second clustering operation is performed on all of the alarms across all frames after they have been mapped into UTM coordinates.…”

Section: The Flir Detectormentioning

confidence: 99%

A queuing model for designing multi-modality buried target detection systems: preliminary results

et al. 2015

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Many remote sensing modalities have been developed for buried target detection, each one offering its own relative advantages over the others. As a result there has been interest in combining several modalities into a single detection platform that benefits from the advantages of each constituent sensor, without suffering from their weaknesses. Traditionally this involves collecting data continuously on all sensors and then performing data, feature, or decision level fusion. While this is effective for lowering false alarm rates, this strategy neglects the potential benefits of a more general system-level fusion architecture. Such an architecture can involve dynamically changing which modalities are in operation. For example, a large standoff modality such as a forward-looking infrared (FLIR) camera can be employed until an alarm is encountered, at which point a high performance (but short standoff) sensor, such as ground penetrating radar (GPR), is employed. Because the system is dynamically changing its rate of advance and sensors, it becomes difficult to evaluate the expected false alarm rate and advance rate. In this work, a probabilistic model is proposed that can be used to estimate these quantities based on a provided operating policy. In this model the system consists of a set of states (e.g., sensors employed) and conditions encountered (e.g., alarm locations). The predictive accuracy of the model is evaluated using a collection of collocated FLIR and GPR data and the results indicate that the model is effective at predicting the desired system metrics.

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On the registration of FLGPR and IR data for a forward-looking landmine detection system and its use in eliminating FLGPR false alarms

Cited by 20 publications

References 0 publications

An apodization approach for processing forward-looking GPR for buried target detection

An apodization approach for processing forward-looking GPR for buried target detection

Multi-band sensor-fused explosive hazards detection in forward-looking ground penetrating radar

A queuing model for designing multi-modality buried target detection systems: preliminary results

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