As the number of space objects in orbit continues to grow, their monitoring and classification have become essential to accurately determine their position and prevent collisions with active satellites. Understanding the surface material of these objects plays a key role, contributing to a more precise understanding of the space environment. While ground-based technologies have seen significant progress, space-based systems offer key advantages. This research proposes a novel approach to classifying space objects using neuromorphic cameras, which offer advantages over conventional sensors, including high dynamic range, low latency, reduced power consumption, and higher temporal resolution. The study investigates the feasibility of using neuromorphic cameras for material classification of space objects. Simulations were conducted using various tools to compare the response of a simulated neuromorphic camera when observing different materials, enabling an initial assessment of their potential for identifying surface composition in space environments.