Space Object Identification and Classification from Hyperspectral
  Material Analysis
Massimiliano Vasile Lewis Walker Andrew Campbell Simao Marto Paul Murray Stephen Marshall Vasili Savitski
This paper presents a data processing pipeline designed to extract
information from the hyperspectral signature of unknown space objects. The
methodology proposed in this paper determines the material composition of space
objects from single pixel images. Two techniques are used for material
identification and classification: one based on machine learning and the other
based on a least square match with a library of known spectra. From this
information, a supervised machine learning algorithm is used to classify the
object into one of several categories based on the detection of materials on
the object. The behaviour of the material classification methods is
investigated under non-ideal circumstances, to determine the effect of
weathered materials, and the behaviour when the training library is missing a
material that is present in the object being observed. Finally the paper will
present some preliminary results on the identification and classification of
space objects.