Information is contained in the light coming from objects, and quite a bit can be learned from studying that light.
Astronomical observations result in many beautiful images if the source is within the resolution limits of instruments. Those limits to resolution are determined by the imaging systems aperture size and the wavelength of the observations. Staying within the visible and infrared spectrum, novel approaches in optics design and processing will be pursued for resolved imaging systems. The primary objective will be the development of imaging systems that can resolve geosynchronous objects.
However, if the object is beyond the resolution limit, other techniques must be used. For a while now photometric light curves have been studied with varying results. One of the objectives of non-resolved imaging is shape detection and/or material identification. There are two primary ways to achieve this identification. One is to search against a catalog of “known” signatures to try to find the best match. The other is to directly invert the signature into a shape and then match the shape to a two-dimensional model-based profile\cite{Lamb}. For either method, the inverse problem solution space is one that is ill defined and the answer is given with some probability of the result.
A question to pose is: Are there ways to narrow down or constrain that solution space?
“Polarimetric observations of space objects can provide information on shape, surface roughness, and electrical conductivity that are very difficult or impossible to obtain from non-polarimetric data.”\cite{Pesses}
One of the primary objectives of Strong EO Imaging is to perform basic research into novel non-resolved imaging approaches and the impacts that multi-spectral polarization has on the inverse problem of non-resolved space object identification.