Früher|Nächster

Performance design of fore-field sensor radiation for an intelligent hyperspectral remote sensing satellite

  • role： First author第一作者
  • Affiliation：

    College of Geoscience and Surveying Engineering, China University of Mining & Technology (Beijing), Beijing 100083, China

    Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, CAS, Beijing 100094, China

  • Email：huangzh1110@126.com
  • Introduction：黄志华，1992年生，男，硕士研究生，研究方向为航天遥感图像的辐射定标和大气校正。E-mail： huangzh1110@126.com
Zhihua HUANG12,  
  • Affiliation：

    College of Geoscience and Surveying Engineering, China University of Mining & Technology (Beijing), Beijing 100083, China

Yueguan YAN1,  
  • role： Corresponding author通信作者
  • Affiliation：

    Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, CAS, Beijing 100094, China

  • Email：zhanghao612@radi.ac.cn
  • Introduction：张浩，1982年生，男，副研究员，研究方向为光学遥感图像辐射定量化。E-mail： zhanghao612@radi.ac.cn
Hao ZHANG2*,  
  • Affiliation：

    Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, CAS, Beijing 100094, China

    University of Chinese Academy of Sciences, Beijing 100049, China

Bing ZHANG23

Resümee

In this study, the sensor bands of a fore-field sensor and the minimum and maximum radiometric range simulation models are discussed by focusing on a tilt angle. The radiation performance design of a fore-field sensor for an intelligent hyperspectral remote sensing satellite with a spectral range of 0.4—1.0 μm is examined. This study provides an important reference for the dynamic range adjustment of the main camera of the hyperspectral remote sensing satellite. The tilt angle of a fore-field sensor is affirmed by considering the processing time, imaging distance, and spatial resolution. The fore-field sensor bands are set by achieving cloud detection, aerosol optical thickness, and water vapor retrieval. The radiance of numerous objects is simulated using MODTRAN. The minimum and maximum radiometric range simulation models are built on the basis of the radiance of several bands with a spectral range of 0.4—1.0 μm. The tilt angle of a fore-field sensor with a width of 100 km is 11° given the response time of 2 s for the fore-viewing sensor processing and main sensor adjustment. The minimum five spectral bands are 0.49, 0.66, 0.87, 0.94, and 2.1 μm. These bands are carefully selected to identify clouds and estimate aerosol parameters and water vapor. The minimum and maximum radiometric range simulation models can be constructed using the radiance of several bands, with a spectral range of 0.4—1.4 μm. The maximum radiance of vegetation, mineral, soil, and man-made environment can be predicted using a radiance with wavelengths of 0.66 and 0.87; 0.49, 0.66, and 0.87; 0.49 and 0.66; and 0.49 μm, respectively. The minimum radiance of vegetation and mineral can be predicted using a radiance with a wavelength of 0.66 μm. The minimum radiance of soil and man-made environment can be predicted using a radiance with a wavelength of 0.49 μm. The verification correlation of the maximum radiometric range simulation model reaches 0.98, with an accuracy maintained at 0.05. The verification correlation of the minimum radiometric range simulation model reaches 0.75, with an accuracy maintained at 0.25. The fore-viewing angle allows the onboard device to have additional time in updating the radiometric dynamic range for the hyperspectral sensor to achieve the cloud detection and parameter retrieval methods. The fore-viewing sensor must obtain at least five spectral bands, namely, 0.49, 0.66, 0.87, 0.94, and 2.1 μm. In the spectral range of 0.4—1.4 μm, the radiance range of the vegetation, mineral, soil, and manmade environment can be predicted using the radiance with wavelengths of the few designed bands. An appropriate radiance range is important for the main camera to adjust its dynamic range by optimizing the gain values. As expected, additional detailed ground surface information can be recorded. In the future, an increasing number of ground objects and other spectral ranges must be investigated.

Schlüsselwort

intelligent hyperspectral;fore-field sensor;radiation transfer model;apparent radiance;radiometric dynamic range

References

  1. 1.
    Chen L F, Li S S, Tao J H and Wang Z T. 2011. Research and Application of Quantitative Retrieval for Aerosol. Beijing: Science Press
  2. 2.
    Cui Z K. 2000. Ocean Earth Surveying and Observing (NEMO) satellite. OME Information, 17(9): 22–29 (
  3. 3.
    Gai L Y, Liu Z J and Zhang J X. 2008. Processing of CHRIS/PROBA hyperspectral data. Engineering of Surveying and Mapping, 17(1): 40–43 (
  4. 4.
    Gao D L. 2011. Germany plans to launch advanced optical payload for earth observation in 2013 – EnMAP. Infrared, 32(3): 47–48 (
  5. 5.
    Gong R. 2015. Sentinel -2A satellite launched. Space International(8): 36–40 (
  6. 6.
    Hou S W, Sun W F and Zheng X S. 2014. Overview of cloud detection methods in remote sensing images. Space Electronic Technology, 11(3): 68–76, 86 (
  7. 7.
    Li C, Gao L R, Plaza A and Zhang B. 2017. FPGA implementation of a maximum simplex volume algorithm for endmember extraction from remotely sensed hyperspectral images. Journal of Real-Time Image Processing [DOI: 10.1007/s11554-017-0679-2]
  8. 8.
    Liang S L. 2009. Quantitative Remote Sensing of Land Surfaces. Beijing: Science Press
  9. 9.
    Liu S C, Gao M F, Liu Q H, Fan Y D and Yang S Q. 2009. Retrieval of water vapor using hyperspectral data. Remote Sensing Information(3): 11–14, 37 (
  10. 10.
    Stelter C, Biering B, Heym B and Chen X J. 2000. Common cabin design for micro satellite BIRD for infrared earth observation. Control Engineering of China(3): 49–57 (Stelter C, Biering B, Heym B,
  11. 11.
    Sun J. 2006. Retrival of Aerosol Optical Depth by Hyperspectral Remote Sensing and its Environmental Impact. Shanghai: East China Normal University
  12. 12.
    Sun L. 2006. Remote sensing of aerosols over urban areas. Beijing: University of Chinese Academy of Science
  13. 13.
    Sun L, Liu Q H, Liu Q and Chen L F. 2006. Aerosol optical thickness retrieving over bright surface: progress and prospect. Progress in Geography, 25(3): 70–78 (
  14. 14.
    Tan B X, Li Z Y, Chen E X and Pang Y. 2005. Processing of EO-1 Hyperion hyperspectral data. Remote Sensing Information(6): 36–41 (
  15. 15.
    Tong Q X, Zhang B and Zhang L F. 2016. Current progress of hyperspectral remote sensing in China. Journal of Remote Sensing, 20(5): 689–707 (
  16. 16.
    Wang Y M, Lang J W and Wang J Y. 2013. Status and prospect of space-borne hyperspectral imaging technology. Laser&Optoelectronics Progress, 50(1): 010008 (
  17. 17.
    Xie C. 2014. Construction and optoelectronic properties of graphene and silicon nanostructures. Hefei: Hefei University of Technology
  18. 18.
    Yang L L, Zhang T B, He J H, Yue Y Z and Zhang Y C. 2015. Research on the Hyperion data pre-processing method based on ENVI. Journal of Henan Science and Technology(9): 39–42 (
  19. 19.
    Yue Z G. 2013. NASA conducts pre-airborne tests on HyspIRI satellite missions. Infrared, 34(12): 13 (
  20. 20.
    Zeng Q, Zhao Y, Tian L Q and Chen X L. 2013. Evaluation on the atmospheric correction methods for water color remote sensing by using HJ-1A/1B CCD image-taking Poyang Lake in China as a case. Spectroscopy and Spectral Analysis, 33(5): 1320–1326 (
  21. 21.
    Zhang B. 2011. Intelligent remote sensing satellite system. Journal of Remote Sensing, 15(3): 415–431 (
  22. 22.
    Zhang B. 2016. Advancement of hyperspectral image processing and information extraction. Journal of Remote Sensing, 20(5): 1062–1090 (
  23. 23.
    Zhang L. 2015. High spectral band selection and application inquiry for the observation of the multi-mode. Xi`an: Xi`an University of Science and Technology
  24. 24.
    Zhang Q, Tang Y L and Qiao Y K. 2012. Progress, application and development of earth observation technology. National Security Geophysics Series(八)——Remote Sensing Geophysics and National Security. Beijing
  25. 25.
    Zhang X. 2016. The study on cloud detection and atmospheric correction with new type satellite data. Nanjing: Nanjing University of Information Science& Technology

Lesen Sie die ganze Passage

The above content is generated by Large Model Translation. The translated content is for reference only. We do not assume any commercial or legal responsibilty for any consequences arising from the use of our website