Modeling EO/IR systems with ASSET: applied machine learning for synthetic WFOV background signature generation

Bryan J. Steward; Bret M. Wagner; Kenneth M. Hopkinson; Shannon R. Young

doi:10.1117/12.2638487

2 November 2022 Modeling EO/IR systems with ASSET: applied machine learning for synthetic WFOV background signature generation

Bryan J. Steward, Bret M. Wagner, Kenneth M. Hopkinson, Shannon R. Young

Author Affiliations +

Proceedings Volume 12271, Electro-optical and Infrared Systems: Technology and Applications XIX; 122710B (2022) https://doi.org/10.1117/12.2638487
Event: SPIE Security + Defence, 2022, Berlin, Germany

Abstract

The AFIT Sensor and Scene Emulation Tool (ASSET) is a physics-based model used to generate synthetic data sets of wide field-of-view (WFOV) electro-optical and infrared (EO/IR) sensors with realistic radiometric properties, noise characteristics, and sensor artifacts. This effort evaluates the use of Convolutional Neural Networks (CNNS) trained on samples of real space-based hyperspectral data paired with panchromatic imagery as a method of generating synthetic hyperspectral reflectance data from wide-band imagery inputs to improve the radiometric accuracy of ASSET. Further, the effort demonstrates how these updates will improve ASSET’s radiometric accuracy through comparisons to NASA’s moderate resolution imaging spectroradiometer (MODIS). In order to place the development of synthetic hyperspectral reflectance data in context, the scene generation process implemented in ASSET is also presented in detail.

Conference Presentation

Citation Download Citation

Bryan J. Steward, Bret M. Wagner, Kenneth M. Hopkinson, and Shannon R. Young "Modeling EO/IR systems with ASSET: applied machine learning for synthetic WFOV background signature generation", Proc. SPIE 12271, Electro-optical and Infrared Systems: Technology and Applications XIX, 122710B (2 November 2022); https://doi.org/10.1117/12.2638487

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