Synthetic thermal image generation and processing for close proximity operations

The new scenarios foreseen in forthcoming space missions have increased interest towards optical-based relative navigation techniques, which have demonstrated efficacy in a variety of operational conditions. Although object detection methods have predominantly been used within the visible spectrum, optical payloads struggle in weak lighting conditions and are susceptible to overexposure. Consequently, thermal imaging systems are being investigated as a potential solution, as their integration into the current systems would greatly extend future mission capabilities. This study seeks to fill the gap in literature by assessing the performance of state-of-the-art object detection algorithms with images captured in the thermal spectrum. Given the scarcity of readily available thermal infrared (TIR) images captured in orbit, a novel rendering pipeline is implemented to generate physically accurate thermal images relevant to close-proximity scenarios. These synthetic representations feature a simplified target spacecraft against Earth and deep space backgrounds, including variations in illumination conditions, material properties, relative state, and scale. To ensure realistic outputs, the radiative field of the Earth is modelled based on satellite measurements collected in the cloud and Earth radiant energy system (CERES) database. To enrich the fidelity of the outputs, a thermal sensor model and the corresponding noise levels are introduced in the pipeline. The generated images are then used to test the performance of traditional object detection algorithms in discerning the region of interest (ROI) under different orbital scenarios. The results demonstrate the effectiveness of the selected methodologies in mitigating the influence of the Earth in the ROI extraction process, while also revealing a performance degradation due to the presence of multi-material targets.