Abstract
We developed a process to provide design recommendations for compact spaceborne spectral imaging systems with adaptive band selection capabilities. Our focus application was subpixel target detection, and we analyzed a set of mission scenarios to find relationships in detection performance between selected parameters of interest. We used an analytic model to predict performance and generate trade curves, then simulated a scene to analyze potential operational effects on performance for the selected target and background combinations. Using these models, we predicted and assessed each scenario to provide recommendations for mission feasibility and system design. The parameters we selected for analysis were target fill fraction, noise, number of bands, and scene complexity to find critical points in the trade space and reach a set of recommendations. We examined the operational effects by simulating a realistic scenario and ensuring key real-world phenomena were captured within the spectral images. Our results produced recommendations for each mission and provided a proof of concept for a process to analyze designs of miniature spaceborne imaging systems.
Highlights
T HERE are many design requirements for an Earth observing remote sensing system, and when using miniature spaceborne systems to collect spectral images, there are several considerations that require analysis
The process and results we present in this article were applicable to compact spaceborne spectral imaging systems with adaptive band selection capabilities deployed for a wide-area search of subpixel targets
From the prediction and assessment of utility we obtained from the eight target and background combinations, we were able to find novel relationships between trade space parameters
Summary
T HERE are many design requirements for an Earth observing remote sensing system, and when using miniature spaceborne systems to collect spectral images, there are several considerations that require analysis. The trade space for optimizing the image quality resides mostly in the spatial domain. The Manuscript received September 6, 2019; revised January 13, 2020 and May 9, 2020; accepted May 10, 2020. Date of publication May 12, 2020; date of current version July 17, 2020. Shawn Higbee and Lawrence Siegel are with the Department of Defense and Security, Lawrence Livermore National Laboratory, Livermore, CA 94550 USA
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