Modeling EO/IR systems with ASSET: spectral super-resolution for synthetic WFOV background signature generation

James D. Menezes; Ayden M. Coughlin; Bret M. Wagner; Bryan J. Steward; Shannon R. Young

doi:10.1117/12.2680274

23 October 2023 Modeling EO/IR systems with ASSET: spectral super-resolution for synthetic WFOV background signature generation

James D. Menezes, Ayden M. Coughlin, Bret M. Wagner, Bryan J. Steward, Shannon R. Young

Author Affiliations +

Proceedings Volume 12736, Target and Background Signatures IX; 1273605 (2023) https://doi.org/10.1117/12.2680274
Event: SPIE Security + Defence, 2023, Amsterdam, Netherlands

Abstract

The Air Force Institute of Technology (AFIT) created the AFIT Sensor and Scene Emulation Tool (ASSET) which aims to produce accurate and realistic electro-optical and infrared (EO/IR) data. While working to validate ASSET’s cloud free radiometry calculations, researchers demonstrated that the radiometric accuracy of synthetic data can be improved using Hyperspectral Imagery (HSI). This research addresses the lack of accurate HSI reflectance data required by ASSET to perform scene generation with two novel machine learning (ML) models and a scene generation process. Two Convolutional Neural Network (CNN) models, a U-Net and a Pix2Pix Generative Adversarial Network (GAN), are trained using multi-sensor data including land classification, elevation, texture, and HSI image data. The ML model processes image chips as inputs to a novel rendering process, generating realistic whole-Earth hyperspectral reflectance maps between 480 nm and 2500 nm. To study the accuracy of this model and rendering process, generated data was compared against truth HSI data using Mean Absolute Error (MAE), Mean Squared Error (MSE), and image quality metrics, such as Structural Similarity (SSIM) and Peak-Signal-to-Noise-Ratio (PSNR). This paper details the current stage of model development and the possible contributions of the model to ASSET and synthetic scene generation.

Conference Presentation

Citation Download Citation

James D. Menezes, Ayden M. Coughlin, Bret M. Wagner, Bryan J. Steward, and Shannon R. Young "Modeling EO/IR systems with ASSET: spectral super-resolution for synthetic WFOV background signature generation", Proc. SPIE 12736, Target and Background Signatures IX, 1273605 (23 October 2023); https://doi.org/10.1117/12.2680274

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available