Abstract：With the increase of space activities in various countries, the number and variety of space objects also gradually increased. How to identify and catalog the space object is a critical research issue in the field of space object surveillance for different countries. The research on non-cooperative space object mainly aims to get the information like surface materials, attitude, shape and critical payload information. And the acquisition of surface materials information is the basis for researching space object optical characteristics as well as state recognition. A multi-color photometric measurement system for space object’s surface materials is set up. To reduce the influence of stray light on the measurement results, the entire system is deployed in an optical darkroom. The light source adopts a solar simulator with spectral grade level A. The detector uses a FieldSpec4 spectrometer manufactured by the ASD company in America. The wavelength range is 350~2 500 nm, and the spectral resolution is 1 nm. The spectrometer’s optical fiber is located on the electrically controlled turntable, which can be able to simulate different observation geometry to obtain various data for the same sample. By using Johnson-Cousins UBVRI five-color spectroscopy system, ten kinds of color-index data of eight common surface materials (e.g., GaAs, anodic Al, anodic Kapton, black paint, epoxy paint, aluminized Kapton, titanium blue paint, white paint) under different observation geometry conditions are measured. And every color-index data includes 30 groups of experimental data. Through the traditional 1-sigma uncertainty box method (namely, for a given material with several groups of experimental data, calculating the mean value and standard deviation for each kind of color-index. Then drawing color-index uncertainty box, based on the mean value as rectangle center and twice of standard deviation as the side length), in the ideal identification situation, four kinds of materials (GaAs, anodic Al, anodic Kapton, titanium blue paint) can be identified through the R-I and B-R color index uncertainty box. Two kinds of materials(epoxy paint, white paint) can be determined through the B-V and B-R color index uncertainty box. That above color-index cannot identify the remaining two materials (black paint and aluminized Kapton); however, there are two main problems in using 1-sigma uncertainty box method. The first one is that it is necessary to know the prior information about which band that the test materials are sensitive to, so as to determine which kind of color-index to be used. The other problem is that the identification rate is easily affected by the number of test samples and has poor reliability. The extreme learning machine (ELM) is a kind of machine learning algorithm that uses randomized hidden layer nodes and least-squares method to train data. The algorithm has the advantages of learning efficiency, good generalization performance and not easily falling into optimal local solution. It is widely used in the data classification and regression analysis. Therefore, the ELM algorithm is introduced to solve the problem. Color-index data are randomly divided into a training dataset and testing dataset according to the proportion of 2∶1. A total of three random experiments are carried out. Each kind of material is numbered in the order of 1∶8 in the training dataset, namely, each number from 1 to 8 has 20 groups color-index data respectively, and each group includes ten kinds of color-index data. As for the training dataset, the same number is assigned to the material according to the known attribution materials type. Regard determination coefficient and calculating time as the judgment indicators to judge the accuracy and real-time capability of ELM algorithm. The results show that: whether identify the single kind of material or all testing dataset, the determination coefficient of training dataset is all above 0.98 and determination coefficient of the testing dataset is above 0.96, meaning that at most three groups color-index data cannot be identified in each experiment. In the aspect of calculating time, the total time can be completed within 0.07 s, even up to 0.002 s. The identification efficiency and reliability are much higher than the traditional 1-sigma uncertainty box method, which shows that the ELM algorithm can accurately and quickly identify space object’s surface materials. Relevant research can provide technical support for state information inversions such as shape, attitude and critical payloads of space objects.
李 鹏，李 智，徐 灿，方宇强，张 峰. 基于超限学习机算法的空间目标材质多色测光识别研究[J]. 光谱学与光谱分析, 2019, 39(02): 363-369.
LI Peng, LI Zhi, XU Can, FANG Yu-qiang, ZHANG Feng. Research on Space Object’s Materials Multi-Color Photometry Identification Based on the Extreme Learning Machine Algorithm. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2019, 39(02): 363-369.
[1] Fulcoly D O, Kalamaroff K I, Chun F K. Journal of Spacecraft and Rockets, 2012, 49(1): 76.
[2] Linares R, Leve F A, Jan M K, et al. Adv. Astronaut Sci., 2012, 144: 41.
[3] XU Can, ZHANG Ya-sheng, ZHAO Yang-sheng, et al(徐 灿, 张雅声, 赵阳生, 等). Spectroscopy Spectral Analysis (光谱学与光谱分析), 2017, 37(3), 672.
[4] Jin Xiaolong, Tang Yijun, Sui Chenghua. Optics and Electronics, 2013, 40(12): 44.
[5] Zhao X F, Zhang H Y, Yu Y, et al. Advances in Space Research, 2016, 58(11): 2269.
[6] Howard M, Klem B, Gorman J. Advanced Maui Optical and Space Surveillance Technologies Conference. Kihei: Maui Economic Devepment, 2015.
[7] WANG Yang, DU Xiao-ping, FAN Chun-lin(王 阳, 杜小平, 范椿林). Chin. Sci. Bull.(科学通报), 2017, 62: 1578.
[8] Singh N, Horwood J T, Aristoff J M, et al. In: Proceedings of the 26th AAS/AIAA Space Flight Mechanics Meeting, Napa: AIAA, 2016.
[9] Huang G B, Zhou H, Ding X, et al. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 2012, 42(2): 513.
[10] ZHANG Dong-hai, LI Gui-rong, LI Ruo-cheng, et al(张东海, 李贵荣, 李若诚, 等). Laser and Optoelectronics Progress(激光与光电子学进展), 2013, 50(4): 183.
