Adaptive coherence estimator (ACE) for explosive hazard detection using wideband electromagnetic induction (WEMI)

Alvey, Brendan; Zare, Alina; Cook, Matthew; Ho, K. C.

doi:10.1117/12.2223347

Cited by 16 publications

(15 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The ACE is a target detection algorithm that carries out a partial unmixing approach to isolate feature of interest from the background and its input is a single score (abundance of the target) per pixel [90]. It is generated from the generalized likelihood ratio (GLR) approach, which is a homogenously most powerful invariant detection statistic [91,92]. The ACE is invariant to the relative scaling of input spectra and has a constant false alarm rate (CFAR) for such scaling [93].…”

Section: Adaptive Coherence Estimator (Ace)mentioning

confidence: 99%

Landsat-8, Advanced Spaceborne Thermal Emission and Reflection Radiometer, and WorldView-3 Multispectral Satellite Imagery for Prospecting Copper-Gold Mineralization in the Northeastern Inglefield Mobile Belt (IMB), Northwest Greenland

Pour

Park

et al. 2019

Remote Sensing

View full text Add to dashboard Cite

Several regions in the High Arctic still lingered poorly explored for a variety of mineralization types because of harsh climate environments and remoteness. Inglefield Land is an ice-free region in northwest Greenland that contains copper-gold mineralization associated with hydrothermal alteration mineral assemblages. In this study, Landsat-8, Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), and WorldView-3 multispectral remote sensing data were used for hydrothermal alteration mapping and mineral prospecting in the Inglefield Land at regional, local, and district scales. Directed principal components analysis (DPCA) technique was applied to map iron oxide/hydroxide, Al/Fe-OH, Mg-Fe-OH minerals, silicification (Si-OH), and SiO2 mineral groups using specialized band ratios of the multispectral datasets. For extracting reference spectra directly from the Landsat-8, ASTER, and WorldView-3 (WV-3) images to generate fraction images of end-member minerals, the automated spectral hourglass (ASH) approach was implemented. Linear spectral unmixing (LSU) algorithm was thereafter used to produce a mineral map of fractional images. Furthermore, adaptive coherence estimator (ACE) algorithm was applied to visible and near-infrared and shortwave infrared (VINR + SWIR) bands of ASTER using laboratory reflectance spectra extracted from the USGS spectral library for verifying the presence of mineral spectral signatures. Results indicate that the boundaries between the Franklinian sedimentary successions and the Etah metamorphic and meta-igneous complex, the orthogneiss in the northeastern part of the Cu-Au mineralization belt adjacent to Dallas Bugt, and the southern part of the Cu-Au mineralization belt nearby Marshall Bugt show high content of iron oxides/hydroxides and Si-OH/SiO2 mineral groups, which warrant high potential for Cu-Au prospecting. A high spatial distribution of hematite/jarosite, chalcedony/opal, and chlorite/epidote/biotite were identified with the documented Cu-Au occurrences in central and southwestern sectors of the Cu-Au mineralization belt. The calculation of confusion matrix and Kappa Coefficient proved appropriate overall accuracy and good rate of agreement for alteration mineral mapping. This investigation accomplished the application of multispectral/multi-sensor satellite imagery as a valuable and economical tool for reconnaissance stages of systematic mineral exploration projects in remote and inaccessible metallogenic provinces around the world, particularly in the High Arctic regions.

show abstract

Section: Adaptive Coherence Estimator (Ace)mentioning

confidence: 99%

Landsat-8, Advanced Spaceborne Thermal Emission and Reflection Radiometer, and WorldView-3 Multispectral Satellite Imagery for Prospecting Copper-Gold Mineralization in the Northeastern Inglefield Mobile Belt (IMB), Northwest Greenland

Pour

Park

et al. 2019

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…A benefit to using the DSRF is that it is directly related to the physical properties of potential targets and is thus invariant to the relative position and orientation of the WEMI sensor. 3,4,14 The dictionary A can be created by selecting a range of possible relaxation frequency terms ζ, and defining each element as a(ω, ζ) = jω/ζ 1 + jωζ (6) It follows that the atom for the k th relaxation frequency can be constructed as…”

Section: Discrete Spectrum Of Relaxation Frequencies Dictionarymentioning

confidence: 99%

“…In this work, ζ was created from 100 equally log-spaced frequencies ranging from just below the minimum to just above the maximum sensor operating frequency. A visual depiction of an 18-frequency DSRF dictionary is presented in the work by Alvey, et al and Cook, et al 3,4 When using the DSRF dictionary for target detection, it is assumed that a target sample can be represented by a linear combination of the dictionary atoms of A with a small amount of additive zero-mean Gaussian noise. Given the fixed dictionary, algorithms can be developed to search for the expected target responses defined in the atoms of A.…”

Section: Discrete Spectrum Of Relaxation Frequencies Dictionarymentioning

confidence: 99%

“…The adaptive cosine estimator (ACE) is an alternative detection statistic which closely resembles SMF. 3,18 The hypotheses utilized by ACE are provided by…”

Section: Adaptive Cosine Estimator (Ace)mentioning

confidence: 99%

“…WEMI sensors have been studied extensively for use in detecting buried, metallic objects. [1][2][3][4][5] These sensors operate by sending a time-varying electromagnetic field through the ground via a transmit coil. Conductive subsurface objects are energized by this field and quickly return to resting states.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Comparison of hand-held WEMI target detection algorithms

McCurley

Bocinsky

Zare

2019

Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XXIV

Self Cite

View full text Add to dashboard Cite

Wide-band Electromagnetic Induction Sensors (WEMI) have been used for a number of years in subsurface detection of explosive hazards. While WEMI sensors have proven effective at localizing objects exhibiting large magnetic responses, detecting objects lacking or containing very low amounts of conductive materials can be challenging. In this paper, we compare a number of target detection algorithms in the literature in terms of detection performance. In the comparison, methods are tested on two real-world data sets: one containing relatively low amounts of ground noise pollution, and the other demonstrating highly-magnetic soil interference. Results are quantitatively evaluated through receiver-operator characteristic (ROC) curves and are used to highlight the strengths and weaknesses of the compared approaches in hand-held explosive hazard detection.rely on observing high-magnitude responses to flag potential threats, 6 Scott, et al. proposed that the relaxation energy emitted by conductive objects can be modeled as a function of operating frequency and used to created a dictionary of expected target responses. 7-9 Elaboration on the discrete spectrum of relaxation frequencies (DSRF) target dictionary and its exploitation for use in WEMI hazard detection is provided in Section 3.1.While there have been many successes in algorithm development for detection of high-metal targets, EHD is certainly not a solved problem. The first obstacle making EHD difficult is that there exists a wide assortment of targets to be detected, ranging from small anti-personal to large anti-tank. Additionally, metal content can range from high to low, or even even be absent in the case of plastic targets. While it has long been assumed that the inherent properties exhibited by metal detectors make discovery of lowly and non-conductive objects difficult to achieve, recent work has shown that the utilization of a novel WEMI processing procedure may provide discriminative information for unmasking difficult targets. Further description of this processing as well an initial investigation to the use of its generated features for hazard discovery is presented in Sections 3.6 and 4.4.3, respectively. In addition to the range of targets, non-target objects (clutter) and natural variations in the soil's electromagnetic properties can bring rise to false alarms (FA) where nontarget objects are labeled as potential threats. A principal challenge in EHD is mitigating false alarms while not masking true detections, or true positives (TP).In this paper, the authors compare an assortment of WEMI prescreeners which have shown considerable success as subsurface object detectors on alternative WEMI hazard discovery systems. Experiments were conducted to 1.) gauge the performance of target detectors across a variety of WEMI sensors, 2.) compare prescreeners utilizing pre-defined versus learned target concepts, and 3.) evaluate two pre-processing methods for interference removal. Quantitative evaluation of performance is given by receiver-operating characte...

show abstract