Lawrence Livermore National Laboratory



Alex Pertica

Overview

Monolithic (non-segmented) telescopes have been demonstrated to be a compelling form factor for space telescopes due to the design’s robust, compact and athermal performance. A visible-band monolithic telescope developed at LLNL was flown on a CubeSat (a miniature satellite) in low Earth orbit and the imaging payload achieved diffraction-limited imaging over all portions of the orbital regime with no onboard focus or alignment mechanisms. Although pan-chromatic visible-band imaging systems play a vital role for a number of science and security missions, additional performance outside of the visible passband or with multi- or hyper-spectral performance would enable many new missions. This project explored and advanced the utility of monolithic telescopes in two important ways: understanding how to maintain high levels of performance when operating at wavelengths beyond the visible and learning how to improve Earth observation missions with small satellite multi- and hyperspectral capabilities.

Impact on Mission

Our spectral applications research has advanced the understanding of and contributed to the toolkit of spectral imaging systems analyses. Our investigations set the stage for further research in specific telescope configurations. Of particular note is the potential application of the background complexity and detectability metrics as measures of spectral image quality for archived spectral images, as well as metrics for selecting parameters for future imagery collections. Another promising area of research is the extension of the analysis methodology to include the temporal dimension to look for change detection. Other possible future research topics include quantifying the error sensitivity of the system recommendations and using the developed tools to determine a confidence level for detections made in empirical imagery.

Publications, Presentations, Etc.

Han, S., et al., 2018. "Simulation Techniques for Image Utility Analysis," Proceedings of Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXIV, SPIE Vol. 10644, 106440P, April 2018. LLNL-CONF-747280.