Multiple instance hybrid estimator for learning target signatures

Jiao, Changzhe; Zare, Alina

doi:10.1109/igarss.2017.8127120

Cited by 8 publications

(3 citation statements)

References 13 publications

(22 reference statements)

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“…Expanding upon our work in [20], the MI-HE algorithm and experiments presented in this paper maintain the following improvements and advantages in comparison to our prior work: (1) introducing a discriminative concept learning term; (2) improved gradient descent optimization using Armijo's rule; (3) comprehensive experiments on multiple concepts learning (both simulated and realistic) and more types of targets for Gulfport data; (4) comprehensive comparison with state-of-the-art MIL algorithms; (5) analysis of MI-HE's robustness to parameter setting.…”

Section: Introductionmentioning

confidence: 88%

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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Section: Introductionmentioning

confidence: 88%

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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“…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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“…In this chapter, we present the proposed multiple instance hybrid estimator (MI-HE) [74,75] framework. MI-HE is a discriminative target concept learning algorithm for problems with mixed data and label uncertainty.…”

Section: Chapter 4 Multiple Instance Hybrid Estimatormentioning

confidence: 99%

Target concept learning from ambiguously labeled data

Jiao¹

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The multiple instance learning problem addresses the case where training data comes with label ambiguity, i.e., the learner has access only to inaccurately labeled data. For example, in target detection from remotely sensed hyperspectral imagery, targets are usually sub-pixel and the ground truthing of the targets according to GPS coordinates could drift across several meters. Thus the locations of the targets corresponding to the hyperspectral image are inaccurate. Training a supervised algorithm or extracting target signatures from this kind of labels is intractable. This dissertation investigates the topic target concept learning from ambiguously labeled data comprehensively; reviews and proposes several methods that either learn a set of representative or discriminative target concepts. The multiple instance hybrid estimator (MI-HE) maximizes the response of the hybrid detector under a generalized mean framework and estimates a set of discriminative target concepts. MI-HE adopts a linear mixture model and iterates between estimating a set of discriminative target and non-target signatures and solving a sparse unmixing problem. MI-HE preserves bag-level label information for each positive bag and is able to estimate a target concept that is commonly shared among positive bags. Furthermore, MI-HE has the potential to learn multiple signatures to address signature variability. After learning target concept, signature based detector could be applied for target detection. The presented algorithms were tested in many applications including simulated and real hyperspectral target detection, heartbeat characterization from ballistocardiogram signals and tree species classification from remotely sensed data. The presented algorithms were proven to be effective in learning high-quality target signatures and consistently achieved superior performance over the state-of-the-art comparison algorithms.

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Multiple instance hybrid estimator for learning target signatures

Cited by 8 publications

References 13 publications

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

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

Comparison of hand-held WEMI target detection algorithms

Target concept learning from ambiguously labeled data

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