For a long time, the United States and other military powers have been committed to develop their space situational awareness (SSA). The monitoring system of ground space target is an important part of the space situational awareness. Monitoring and identifying of space targets are mainly due to a large number of space debris make the main body of the satellite face some unknown risks. To avoid the space junk and enhance the ability of the satellite identifying the space objects, it is very important to ensure safety of the spacecraft in orbit. In the observation of space debris, because of the compact structure, complex material, and spatial resolution of ground observation equipments, a variety of materials are in the same pixel usually, namely “mixed pixel”. The current researches on mixed pixel mainly focus on obtaining pure components of mixed pixels and the corresponding abundance, but they often neglect that hyperspectral data for determining the number of pure substances is very important for mixed pixels without any prior information analysis. If the estimated number is too small, the extracted endmembers are still mixed pixels; if the number of endmembers is too large, the extracted endmembers may still contain noise components. Based on the spectral linear mixture model, this paper proposes an improved p norm pure pixel identification algorithm. The method is mainly based on the characteristics of spectral data which are similar with those of low dimensional manifolds. Firstly, according to the principle of orthogonal projection, the extracted endmembers are extended to the orthogonal projection operator. By analyzing the p norm of each projected pixel vector, the number of the p norm value higher than the threshold in the vectors is considered to be the number of pure materials. The simulation experiments are carried out by using the commonly used space materials data and the United State Geological Survey database. The experimental results show that the proposed method can not only estimate the number of pure materials, but also extract spectra of the pure materials in the target, which improves the automation of spectral unmixing process to a certain extent. Compared with some existing algorithms, this method has strong robustness and can estimate the correct number of space debris in the case of low SNR. Therefore, the proposed algorithm can greatly improve the feasibility in determining the type and number of materials according to the space debris spectrum.