Doyle Hall (Boeing LTS)
Keywords: satellite characterization, spectral unmixing, material identification
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 in a single spectral band, they provide little information on the types of materials covering the satellites various surfaces. Detailed surface material characterization generally requires spectrometric and/or multi-band photometric measurements. Fortunately, many instruments provide such multi-band information (e.g., spectrographs and multi-channel photometers). However, these sensors often measure the brightness of 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 or inversion process must be employed to determine the materials covering each of the satellite s individual sub-components. The first section of this paper describes the inversion theory required to retrieve satellite surface material properties from temporal sequences of whole-body multi-band brightness measurements. The inversion requires 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 component capable of reflecting sunlight, 3) the satellites attitude, specifying the bodys orientation at the time of each multi-band measurement, and 4) a database of bi-directional reflection distribution functions for a set of candidate surface materials. As output, the inversion process yields estimates of the fraction of each major satellite component covered by each candidate material. The second section of the paper describes several tests of the method by applying it to simulated multi-band observations of a cub
Date of Conference: September 14-17, 2010
Track: Non-resolved Object Characterization