Functions of Multiple Instances for Learning Target Signatures

Jiao, Changzhe; Zare, Alina

doi:10.1109/tgrs.2015.2406334

Cited by 48 publications

(47 citation statements)

References 51 publications

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“…For this reason, WEMI detection tasks adhere well to multiple instance learning (MIL) models. 4,[18][19][20][21][22][23] Multiple instance learning is a variation on supervised learning for problems with uncertain or imprecisely-labeled training data. 18 Instead of pairing each training sample with a class label, MIL methods learn from a set of labeled concepts called "bags".…”

Section: Multiple Instance Ace (Mi-ace)/ Smf (Mi-smf)mentioning

confidence: 99%

Comparison of hand-held WEMI target detection algorithms

McCurley

Bocinsky

Zare

2019

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

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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...

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Section: Multiple Instance Ace (Mi-ace)/ Smf (Mi-smf)mentioning

confidence: 99%

Comparison of hand-held WEMI target detection algorithms

McCurley

Bocinsky

Zare

2019

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

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“…4 When the current point is labeled as a target point (i.e., representing a target of interest) the linear combination can be written as…”

Section: Extended Functions Of Multiple Instancesmentioning

confidence: 99%

Multiple instance dictionary learning for subsurface object detection using handheld EMI

et al. 2015

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A dictionary learning approach for subsurface object detection using handheld electromagnetic induction (EMI) data is presented. A large number of unsupervised and supervised dictionary learning methods have been developed in the literature. However, the majority of these methods require data point-specific labels during training. In the application to subsurface object detection, often the specific training data samples that correspond to target and non-target are not known and difficult to determine manually. In this paper, a dictionary learning method that addresses this issue using the multiple instance learning techniques is presented. Results are shown on real EMI data sets.

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“…Several previous methods for target characterization have been developed in the literature. The FUnctions of Multiple Instance (FUMI) algorithms [17], [18] learn representative concept from reconstruction error of the uncertainly labeled data. Compared with FUMI, MI-HE learns a discriminative target concept that maximizes the detection response of possible target regions so the estimated signatures are more discriminative for target detection.…”

Section: Introductionmentioning

confidence: 99%

Multiple instance hybrid estimator for hyperspectral target characterization and sub-pixel target detection

Jiao

Chen

McGarvey

et al. 2018

ISPRS Journal of Photogrammetry and Remote Sensing

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The Multiple Instance Hybrid Estimator for discriminative target characterization from imprecisely labeled hyperspectral data is presented. In many hyperspectral target detection problems, acquiring accurately labeled training data is difficult. Furthermore, each pixel containing target is likely to be a mixture of both target and non-target signatures (i.e., sub-pixel targets), making extracting a pure prototype signature for the target class from the data extremely difficult. The proposed approach addresses these problems by introducing a data mixing model and optimizing the response of the hybrid sub-pixel detector within a multiple instance learning framework. The proposed approach iterates between estimating a set of discriminative target and non-target signatures and solving a sparse unmixing problem. After learning target signatures, a signature based detector can then be applied on test data. Both simulated and real hyperspectral target detection experiments show the proposed algorithm is effective at learning discriminative target signatures and achieves superior performance over state-of-the-art comparison algorithms.

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Functions of Multiple Instances for Learning Target Signatures

Cited by 48 publications

References 51 publications

Comparison of hand-held WEMI target detection algorithms

Comparison of hand-held WEMI target detection algorithms

Multiple instance dictionary learning for subsurface object detection using handheld EMI

Multiple instance hybrid estimator for hyperspectral target characterization and sub-pixel target detection

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