Studying the Potential of Hyperspectral Unmixing for Extracting Composition of Unresolved Space Objects using Simulation Models Jiarui Yi, Miguel Velez-Reyes, and Hector Erives Sensor and Signal Analytics Laboratory Department of Electrical and Computer Engineering, The University of Texas at El Paso, 500 W University Ave, El Paso, TX, USA 79968 e-mail: jyi2@utep.edu, mvelezreyes@utep.edu, herivescon@utep.edu ABSTRACT Space assets are critical for USA defense, security and economic wealth. Remote sensing is an important technology to gain situational awareness of the environment surrounding space assets. Ground-based space telescope technology cannot spatially resolve objects in space that are distant (orbits beyond 1,000 km altitude, e.g. GEO) or that are small (e.g. CubeSats). These objects are denoted as unresolved space objects (URSO). Hyperspectral remote sensing has been proposed as a technology to extract quantitative information about unresolved space objects. The high spectral resolution of hyperspectral sensors contains information about the material composition of the unresolved object from materials’ contribution to the measured spectra. Even though the object cannot be spatially resolved, it may be spectrally resolved. Hyperspectral unmixing is a technique used to decompose mixed measured spectral signatures into the spectral signatures of constituent materials and their abundances. In terrestrial applications, unmixing has been widely studied looking at images that contain spectral and spatial information of the object of interest. In the case of unresolved space objects, the authors have proposed the use of the spectro-temporal signature of temporal traces collected while the space object is in transit in the field of view of the hyperspectral sensor to extract material composition information. A big challenge for this approach is that the collected spectro-temporal signature may not be rich enough to extract the material composition using blind hyperspectral unmixing methods. In this paper, we use a simple simulation model of a satellite like object rotating over a background to study how spatial resolution affects the identifiability of URSO material composition. We look at the performance as a function of the spatial resolution in the quality of extracted endmembers and their abundance. Preliminary results show that increase spatial resolution increase identifiability (not a surprising result) but also that few pixels may be sufficient to identify the material composition if the spectro-temporal signature is rich enough. Keywords: Unresolved Space Object; Hyperspectral Unmixing; Spectro-temporal Signatures; Space Domain Awareness. 1. INTRODUCTION United States is dependent economically and militarily on space assets [1, 2]. Orbiting satellites provide a multitude of services, which are critical for US’s military dominance and economic wealth. Space domain awareness (SDA) is needed to have a clear picture of the environment surrounding US and allied space assets to detect any changes or potential threats. Remote sensing is a key technology for SDA. Remote sensing data for SDA comes primarily from radar and optical systems [3]. Current ground-based space telescope technology cannot spatially resolve objects in space that are distant (e.g., GEO or XGEO) or that are small (e.g., CubeSats, Parasite Satellites). Radar is primarily used for observing targets in LEO while optical ground assets are used to assess the environment at higher orbits. Current state of the art in optical remote sensing for SDA uses photometric light curves, which show the intensity of light radiated by an URSO over time observed in a specific viewing geometry. Its temporal variability is due to the superposition of shape, attitude, motion, and material composition of an object under a specific viewing and illumination geometry [4]. Multispectral observations (or color photometry) with defined standard sets of passbands provide multispectral light curve observations used for multispectral analysis [5]. Hyperspectral remote sensor such as SPICA [6]or SpeX [7] collect spectroscopic observations of space objects over Copyright © 2021 Advanced Maui Optical and Space Surveillance Technologies Conference (AMOS) – www.amostech.com