Multisensor fusion of FLGPR and thermal and visible-spectrum cameras for standoff detection of buried
objects

Pinar, Anthony J.; Havens, Timothy C.; Webb, Adam

doi:10.1117/12.2262674

Cited by 2 publications

(2 citation statements)

References 5 publications

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“…Similarly, Abbott et al (2020) created an effective detection algorithm by utilizing red, green, blue (RGB) imagery for translating non-corresponding LWIR/RGB datasets due to the lack of standalone IR annotated data. Pinar et al (2017) added in a forward-looking ground penetrating radar (GPR) and light detection and ranging (LIDAR) in unison with their visible and thermal camera, improving the signal-to-noise-ratio and thus increasing the buried target classification accuracy.…”

Section: Background Environmental Phenomenological Impacts On Thermal...mentioning

confidence: 99%

Modernizing environmental signature physics for target detection—Phase 3

Clausen¹,

Felt²,

Musty³

et al. 2022

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The present effort (Phase 3) builds on our previously published prior efforts (Phases 1 and 2), which examined methods of determining the probability of detection and false alarm rates using thermal infrared for buried object detection. Environmental phenomenological effects are often represented in weather forecasts in a relatively coarse, hourly resolution, which introduces concerns such as exclusion or misrepresentation of ephemera or lags in timing when using this data as an input for the Army’s Tactical Assault Kit software system. Additionally, the direct application of observed temperature data with weather model data may not be the best approach because metadata associated with the observations are not included. As a result, there is a need to explore mathematical methods such as Bayesian statistics to incorporate observations into models. To better address this concern, the initial analysis in Phase 2 data is expanded in this report to include (1) multivariate analyses for detecting objects in soil, (2) a moving box analysis of object visibility with alternative methods for converting FLIR radiance values to thermal temperature values, (3) a calibrated thermal model of soil temperature using thermal IR imagery, and (4) a simple classifier method for automating buried object detection.

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Section: Background Environmental Phenomenological Impacts On Thermal...mentioning

confidence: 99%

Modernizing environmental signature physics for target detection—Phase 3

Clausen¹,

Felt²,

Musty³

et al. 2022

View full text Add to dashboard Cite

show abstract

“…Similarly, Abbott et al (2020) created an effective detection algorithm by utilizing RGB imagery for translating noncorresponding LWIR/RGB datasets due to the lack of standalone IR annotated data. Pinar et al (2017) added in a forward-looking ground-penetrating radar (GPR) and lidar in unison with their visible and thermal camera, improving the signal-to-noise ratio (SNR) and thus increasing classification accuracy of the buried target.…”

Section: Multisensor Fusion Including Thermal Irmentioning

confidence: 99%

Buried-object-detection improvements incorporating environmental phenomenology into signature physics

Clausen¹,

Truong²,

Bragdon³

et al. 2022

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The ability to detect buried objects is critical for the Army. Therefore, this report summarizes the fourth year of an ongoing study to assess environ-mental phenomenological conditions affecting probability of detection and false alarm rates for buried-object detection using thermal infrared sensors. This study used several different approaches to identify the predominant environmental variables affecting object detection: (1) multilevel statistical modeling, (2) direct image analysis, (3) physics-based thermal modeling, and (4) application of machine learning (ML) techniques. In addition, this study developed an approach using a Canny edge methodology to identify regions of interest potentially harboring a target object. Finally, an ML method was developed to improve automatic target detection and recognition performance by accounting for environmental phenomenological conditions, improving performance by 50% over standard automatic target detection and recognition software.

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Multisensor fusion of FLGPR and thermal and visible-spectrum cameras for standoff detection of buried objects

Cited by 2 publications

References 5 publications

Modernizing environmental signature physics for target detection—Phase 3

Modernizing environmental signature physics for target detection—Phase 3

Buried-object-detection improvements incorporating environmental phenomenology into signature physics

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