Hyperspectral extensions in the MuSES signature code

Pereira, W. E.; Less, David M.; Rodriguez, Leonard; Curran, Allen; Bernstein, Uri; Kwan, Yit-Tsi

doi:10.1117/12.783933

Cited by 2 publications

(2 citation statements)

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“…ThermoAnalytics is the developer of MuSES, the physics-based EO/IR signature prediction software. [5][6][7] MuSES, and its commercial counterpart TAITherm, have been in active development for 25 years through USG support and significant IRAD investment.…”

Section: Description Of the Eo/ir Analysis Software For Prediction Of...mentioning

confidence: 99%

Automatic detection and recognition of humans with YOLO-based deep learning using MuSES-generated EO/IR synthetic imagery

Klein,

Edel,

Packard

et al. 2024

Synthetic Data for Artificial Intelligence and Machine Learning: Tools, Techniques, and Applications II

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Machine learning algorithms are capable of processing image-based scenes, detecting and recognizing embedded targets. This has been demonstrated by data scientists and computer vision engineers, but performant algorithms must be robustly trained to successfully complete such a complex task. This typically requires a large set of training data on which the algorithm can base statistical predictions. Electro-optical infrared (EO/IR) remote sensing applications necessitate a substantial image database with suitable variation for adept learning to occur. For human detection/recognition applications diversity in clothing ensembles, pose, season, times of day, sensor platform perspectives, scene backgrounds and weather conditions can be included in training image sets to ensure sufficient input variety. However, acquiring such a diverse image set from measured sources can be a challenge, especially in thermal infrared wavebands (e.g., MWIR and LWIR). Alternatively, generating synthetic imagery with appropriate features is possible and has been shown to perform well, but a careful methodology must be followed if robust training is to be accomplished. In this work, MuSES and CoTherm are used to generate synthetic EO/IR remote sensing imagery of various human dismounts with a range of clothing, poses and environmental factors. The performance of a YOLO (“you only look once”) deep learning algorithm is studied, and sensitivity conclusions are discussed.

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Section: Description Of the Eo/ir Analysis Software For Prediction Of...mentioning

confidence: 99%

Automatic detection and recognition of humans with YOLO-based deep learning using MuSES-generated EO/IR synthetic imagery

Klein,

Edel,

Packard

et al. 2024

Synthetic Data for Artificial Intelligence and Machine Learning: Tools, Techniques, and Applications II

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“…The EO/IR simulation process in MuSES [14][15][16][17][18][19] involves a progression of sequential steps (see Figure 2) that ultimately lead to sensor radiance prediction. Initially, a transient thermal simulation of the target/background scene is completed, since realistic infrared scene simulation requires the accurate prediction of physical temperatures for both targets and backgrounds.…”

Section: Description Of Eo/ir Scene Simulation Software For Predictio...mentioning

confidence: 99%

Impact study on MuSES-generated EO/IR synthetic imagery for machine learning algorithm development

Packard,

Klein,

Klaschus

et al. 2023

Artificial Intelligence for Security and Defence Applications

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The capabilities of machine learning algorithms for observing image-based scenes and recognizing embedded targets have been demonstrated by data scientists and computer vision engineers. Performant algorithms must be well-trained to complete such a complex task automatically, and this requires a large set of training data on which to base statistical predictions. For electro-optical infrared (EO/IR) remote sensing applications, a substantial image database with suitable variation is necessary. Numerous times of day, sensor perspectives, scene backgrounds, weather conditions and target mission profiles could be included in the training image set to ensure sufficient variety. Acquiring such a diverse image set from measured sources can be a challenge; generating synthetic imagery with appropriate features is possible but must be done with care if robust training is to be accomplished. In this work, MuSES™ and CoTherm™ are used to generate synthetic EO/IR remote sensing imagery of various high-value targets with a range of environmental factors. The impact of simulation choices on image generation and algorithm performance is studied with standard computer vision deeplearning convolutional neural networks and a measured imagery benchmark. Differences discovered in the usage and efficacy of synthetic and measured imagery are reported.

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Hyperspectral extensions in the MuSES signature code

Cited by 2 publications

References 0 publications

Automatic detection and recognition of humans with YOLO-based deep learning using MuSES-generated EO/IR synthetic imagery

Automatic detection and recognition of humans with YOLO-based deep learning using MuSES-generated EO/IR synthetic imagery

Impact study on MuSES-generated EO/IR synthetic imagery for machine learning algorithm development

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