This paper presents preliminary results on the characterization of DirecTV-10 satellite with photometric observations during a glint season from 04:00 – 08:00 UTC on 23 February 2021 with U.S. Air Force Academy’s USAFA-16 telescope and simulations of the scene with the physics-based simulator; Digital Imaging and Remote Sensing Image Generation (DIRSIGTM) developed by the Rochester Institute of Technology (RIT). The objective of this work is to find the best set of operator-tuned parameters needed by the simulator to match as close as possible to observations. To accomplish this, the parameters of the optical system, the latitude/longitude and altitude of the telescope, the two-line element (TLE) set of the satellite, and atmospheric conditions at the time of the observation are input into DIRSIGTM to carry out the simulations. Furthermore, it is assumed that all parameters remain constant throughout the observations. The optical system USAFA-16 is a small aperture telescope equipped with a filter wheel which provides photometric, spectroscopic, and polarimetric images of the satellite. The results reported in this paper consist of an effort to correlate wide-band photometric images of the satellite with simulated images of these same wavebands. We use a high-fidelity CAD model of the satellite, and material properties such as pristine reflectance values, and BRDF measurements of the many components of the model which are provided by the Air Force Research Laboratory (AFRL), and ancillary information. We show preliminary results that demonstrate that DIRSIGTM may be used to characterize the satellite to some degree through the process of correlating calibrated magnitude patterns observed on photometric images. Further investigation is required to do the search of parameters in a systematic way, and move towards better agreement between observed and simulated data.
Hyperspectral remote sensing has been proposed as a method to extract quantitative information about resident space objects (RSO) for space domain awareness. Measured spectral signatures can be used to extract information about material composition, satellite pose, satellite classification, and other quantities about the state of a RSO. This is particularly of interest to extract information of unresolved RSOs (URSO) as the high spectral resolution can help us resolve the object spectrally even though it is not resolved spatially. A challenge is the limited amount of spectral data available for algorithm development, testing and validation. Physics-based modeling and simulation tools such as the Digital Imaging and Remote Sensing Image Generation (DIRSIG™) can help us develop an understanding of RSO spectral signatures and to generate spectral signature databases for design, testing and validation of exploitation algorithms. This paper presents preliminary results of simulated resolved and unresolved imagery of the DirecTV-10 and AMC-1 satellites using DIRSIG™. Simulation results illustrate the spatial, spectral and temporal variability of the spectral signatures for both multi-spectral and hyperspectral signatures as well as mixing phenomena when going from resolved to unresolved imagery. Simulated data can help us develop an understanding of RSO behavior that can inform design, development and testing of algorithms for image exploitation for SDA.
Ground-based remote sensing is an important technology to gain situational awareness of the environment surrounding space assets. Ground-based optical telescopes 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 resident space objects (URSO). Hyperspectral remote sensing has been proposed as a technology to extract quantitative information about URSOs. The high spectral resolution of hyperspectral sensors contains information about URSO material composition. Even though the object cannot be spatially resolved, it may be spectrally resolved. Simulation models provide an alternative to the limited access to real data for algorithm testing and validation. They also provide a platform to perform “controlled” experiments to understand algorithm performance before processing real observations. Here we will present our work in combining tools such as MATLAB, STK and DIRSIG to develop simulation models of different levels of complexity to generate data sets to support remote sensing algorithm testing and validation.
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