Doyle Hall (Boeing LTS), Kris Hamada (Pacific Data Solutions), Thomas Kelecy (Boeing LTS), Paul Kervin (Air Force Research Laboratory)
Keywords: Non-Resolved Object Characterization
Abstract:
Ground-based optical and radar sites routinely acquire resolved images of satellites. These resolved images provide the means to construct accurate wire-frame models of the observed body, as well as an understanding of its orientation as a function of time. Unfortunately, because such images are typically acquired at a single wavelength, this kind of analysis provides little or no information on the types of materials covering the satellites various surfaces. Detailed surface material characterization generally requires multi-band radiometric and/or spectrometric measurements. Fortunately, many widely-available instruments provide such multi-band information (e.g., spectrographs and multi-channel photometers). However, these sensors typically measure the brightness of sunlight reflected from the entire satellite, with no spatial resolution at all. Because such whole-body measurements represent a summation of contributions from many reflecting surfaces, an un-mixing analysis process must be employed to characterize the reflectance of the satellites sub-components. The objective of this presentation is to outline the theory required to retrieve satellite surface properties from temporal sequences of whole-body, multi-band brightness measurements, focusing on two newly-developed analysis methods. Both methods require the following as input: 1) a set of multi-band measurements of a satellites reflected-sunlight brightness, 2) the satellites wire-frame model, including each major sub-component capable of reflecting sunlight, 3) the satellites attitude, specifying the orientation of all of the bodys components at the time of each multi-band measurement. In addition, the first method requires laboratory-measured bi-directional reflection distribution functions (BRDFs) for a set of candidate materials covering the satellites surfaces, and yields estimates of the fraction of each major satellite sub-component covered by each candidate material. The second method does not require any pre-tabulated BRDFs, but instead attempts to retrieve BRDFs for each major satellite sub-component from the non-resolved data using a series expansion approach.
Date of Conference: September 11-14, 2012
Track: Non-Resolved Object Characterization