Physics-based synthetic imagery generation of near-surface geo-environments

Andrew C. Trautz; Matthew D. Bray; Justin T. Carrillo; Orie M. Cecil; John G. Monroe; Matthew W. Farthing; Stacy E. Howington

doi:10.1117/12.3031737

1 November 2024 Physics-based synthetic imagery generation of near-surface geo-environments

Andrew C. Trautz, Matthew D. Bray, Justin T. Carrillo, Orie M. Cecil, John G. Monroe, Matthew W. Farthing, Stacy E. Howington

Author Affiliations +

Proceedings Volume 13199, Target and Background Signatures X: Traditional Methods and Artificial Intelligence; 1319908 (2024) https://doi.org/10.1117/12.3031737
Event: Security + Defence, 2024, Edinburgh, United Kingdom

Abstract

Training object detection algorithms to operate in complex geo-environments remains a significant challenge, necessitating large and diverse datasets (i.e., unique backgrounds and conditions) that are not always readily available. Physically generating requisite data can also be both cost and time prohibitive depending on the object(s) and area(s) of interest, especially in the case of multi-spectral and hyper-spectral imagery. Thus, there is increasing interest in the use of synthetic data to supplement existing physical datasets. To this end, the US Army Engineer Research and Development Center (ERDC) continues to develop a computational test-bed with a tool suite called the VESPA or, the Virtual Environmental Simulation for Physics-based Analysis, to support synthetic multi-spectral and hyper-spectral EO/IR imagery generation. The VESPA consists of integrated (1) scene generation tools, (2) multi-fidelity models for simulating heat and mass transfer and atmospheric energy propagation in geo-environments and climates worldwide that are optimized for high performance computing (3) data interrogation utilities, and (4) component-level sensor models capable of producing AI/ML ready near- and far-field imagery that is comparable to that produced by real sensors. This study presents an overview of the VESPA, new advances/capabilities, and results from a recent detailed validation and verification study.

Conference Presentation

Citation Download Citation

Andrew C. Trautz, Matthew D. Bray, Justin T. Carrillo, Orie M. Cecil, John G. Monroe, Matthew W. Farthing, and Stacy E. Howington "Physics-based synthetic imagery generation of near-surface geo-environments", Proc. SPIE 13199, Target and Background Signatures X: Traditional Methods and Artificial Intelligence, 1319908 (1 November 2024); https://doi.org/10.1117/12.3031737

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