Space Object Identification and Classification from Hyperspectral Material Analysis

TL;DR

This paper introduces a pipeline for identifying and classifying space objects using hyperspectral data, combining machine learning and spectral matching to determine material composition and object categories under various conditions.

Contribution

It proposes a novel data processing pipeline that integrates machine learning and spectral matching for space object material analysis from hyperspectral images.

Findings

Material classification is affected by weathering effects.

Library gaps impact classification accuracy.

Preliminary results show promising identification capabilities.

Abstract

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.