Circular and sustainable space: Findings from hyperspectral imaging

Abstract

Clean production and the ecological transition pose significant challenges for international space organizations, which are developing new strategies for recycling space waste products within a circular economy model. Successful recycling initiatives, which could encompass all or only parts of space waste management, would facilitate the reuse of materials that would otherwise be discarded. The present study aimed at testing a method for the identification and categorization of space waste to facilitate the definition of effective sorting and recycling operations in space. In more detail, the study investigated the potential of a sustainable, low-cost method based on hyperspectral imaging (HSI), employing HSI sensors operating in two spectral ranges—shortwave infrared (SWIR) and near-infrared (NIR)—to develop a classification model capable of identifying and sorting space waste for recycling. The findings demonstrate the advantages of using HSI techniques to identify, recognize, and classify various materials, thereby presenting a viable approach aligned with the circular model. Moreover, the proposed approach is non-invasive and non-destructive, eliminating the need for chemicals that could harm the environment. The technique may enable the differentiation of potentially valuable space waste from pollutants, contributing to sustainable waste management and the circular economy.