Volume 45 Issue S1
Jun.  2016
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Xu Rong, Zhao Fei. Spectral scattering inversion method of GEO satellite component[J]. Infrared and Laser Engineering, 2016, 45(S1): 121-126. doi: 10.3788/IRLA201645.S123001
Citation: Xu Rong, Zhao Fei. Spectral scattering inversion method of GEO satellite component[J]. Infrared and Laser Engineering, 2016, 45(S1): 121-126. doi: 10.3788/IRLA201645.S123001

Spectral scattering inversion method of GEO satellite component

doi: 10.3788/IRLA201645.S123001
  • Received Date: 2016-02-17
  • Rev Recd Date: 2016-03-19
  • Publish Date: 2016-05-25
  • The core of the satellite characteristics inversion based on mixed satellite spectra is the mathematical model and inversion algorithm. Theoretical model of spectral mixing was built with experiments conducted to justify the model. First, theoretical analysis of components' spectral scattering model, linear spectral mixing model and unmixing methods of satellite's spectral data was conducted. Then, experiments were designed to measure and calibrate the spectral BRDF of a high-fidelity GEO satellite and its components, while the spectral scattering characteristics of component and material were discussed. Finally, nonnegative constrained least square methods were utilized to unmix the satellite's spectral data, with the largest relative residue less than 10%. Experiment results show that the linear spectral mixing model and nonnegative constrained least square unmixing methods have practical meaning in explaining spectral data of satellites and inversing satellite conditions.
  • [1] Han Yi, Sun Huayan. Advances in space target optical scattering character research[J]. Infrared and Laser Engineering, 2013, 42(3):758-766. (in Chinese) 韩意, 孙华燕. 空间目标光学散射特性研究进展[J]. 红外与激光工程, 2013, 42(3):758-766.
    [2] Tang Yijun, Jiang Xiaoju, Wei Jianyan, et al. Review of optical observations of high apogee space debris[J]. Journal of Astronautics, 2008, 29(4):1094-1098. (in Chinese) 唐轶峻, 姜晓军, 魏建彦, 等. 高轨空间碎片光电观测技术综述[J]. 宇航学报, 2008, 29(4):1094-1098.
    [3] Nicodemus F E. Reflectance nomenclature and directional reflectance and emissivity[J]. Applied Optics, 1970, 9(6):1474-1475.
    [4] Kennedy P K, Keppler K S, Thomas R J, et al. Validation and verification of the Laser Range Safety Tool (LRST)[C]//SPIE, 2003, 4953:143-153.
    [5] Bedard D, Lvesque M, Wallace B. Measurement of the photometric and spectral BRDF of small Canadian satellites in a controlled environment[C]//Proceedings of the Advanced Maui Optical and Space Surveillance Technologies Conference, 2011:1-10.
    [6] Doyle Hall. Surface material characterization from multi-band optical observations[C]//AMOS, 2010:1-15.
    [7] Sun Chengming, Zhao Fei, Yuan Yan. Feature extraction and recognition of non-resolved space object from space-based spectral data[J]. Acta Physica Sinica, 2015, 64(3):3420-3422. (in Chinese) 孙成明, 赵飞, 袁艳. 基于光谱的天基空间点目标特征提取与识别[J]. 物理学报, 2015, 64(3):3420-3422.
    [8] Murray-Krezan J, Inbody W C, Dao P, et al. Algorithms for automated characterization of three-axis stabilized GEOS using non-resolved optical observations[R]. Air Force Research LAB Kirt and AFB NM Space Vehicles Directorate, 2012.
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Spectral scattering inversion method of GEO satellite component

doi: 10.3788/IRLA201645.S123001
  • 1. Academy of Opto-electronics,Chinese Academy of Sciences,Beijing 100094,China;
  • 2. University of Chinese Academy of Sciences,Beijing 100049,China

Abstract: The core of the satellite characteristics inversion based on mixed satellite spectra is the mathematical model and inversion algorithm. Theoretical model of spectral mixing was built with experiments conducted to justify the model. First, theoretical analysis of components' spectral scattering model, linear spectral mixing model and unmixing methods of satellite's spectral data was conducted. Then, experiments were designed to measure and calibrate the spectral BRDF of a high-fidelity GEO satellite and its components, while the spectral scattering characteristics of component and material were discussed. Finally, nonnegative constrained least square methods were utilized to unmix the satellite's spectral data, with the largest relative residue less than 10%. Experiment results show that the linear spectral mixing model and nonnegative constrained least square unmixing methods have practical meaning in explaining spectral data of satellites and inversing satellite conditions.

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