سابِق|تال

CX-6(02) micro-nano satellite super-resolution imaging

  • role： First author第一作者
  • Affiliation：

    Key Laboratory of Computation Optical Imaging Technology, Chinese Academy of Sciences, Beijing 100094, China

    University of Chinese Academy of Sciences, Beijing 100049, China

  • Email：tanzheng@aoe.ac.cn
  • Introduction：谭政，1982年生，男，工程师，研究方向为计算光学成像、遥感数据增强。E-mail: tanzheng@aoe.ac.cn
Zheng TAN12,  
  • role： Corresponding author通信作者
  • Affiliation：

    Key Laboratory of Computation Optical Imaging Technology, Chinese Academy of Sciences, Beijing 100094, China

  • Email：xiangli@aoe.ac.cn
  • Introduction：相里斌，1967年生，男，研究员，研究方向为光学工程、计算光学成像技术。E-mail: xiangli@aoe.ac.cn
Libin XIANG1*,  
  • Affiliation：

    Key Laboratory of Computation Optical Imaging Technology, Chinese Academy of Sciences, Beijing 100094, China

    University of Chinese Academy of Sciences, Beijing 100049, China

Qunbo LYU12,  
  • Affiliation：

    Key Laboratory of Computation Optical Imaging Technology, Chinese Academy of Sciences, Beijing 100094, China

Jianying SUN1,  
  • Affiliation：

    Department Innovation Academy for Microsatellites of Chinese Academy of Sciences, Shanghai 201203, China

Pingfu LI3,  
  • Affiliation：

    Department Innovation Academy for Microsatellites of Chinese Academy of Sciences, Shanghai 201203, China

Shuang GAO3,  
  • Affiliation：

    Department Innovation Academy for Microsatellites of Chinese Academy of Sciences, Shanghai 201203, China

Zengshan YIN3

ملخص

Micro-nano satellite is one of the developing trends of remote sensing technology with the advantages of light-weight, small size, low cost. However, because of the limitation of the volume and weight, the traditional high resolution optical imaging payload with long focal length and large aperture is difficult to apply to earth observation of micro-nano satellite. In order to solve this problem, a super-resolution imaging scheme is demonstrated in this paper. First, in the mode of images acquisition, to obtain multi frame images of the same region, the velocity of imaging relative to the ground should be controlled by satellite attitude. And because the attitude control deviation of satellite is objective, so subpixel displacement information can be generated by pitching, yaw, and rolling random deviations, without to install any other displacement generators; Secondly, In the super-resolution reconstruction algorithm, aiming at solving the problem of prior constraints on variational bayesian super-resolution reconstruction method, we propose a weighted bi-directional difference prior model to overcome the under-constraint of non-edge regions of image due to total variation prior and L1 norm prior, to further restrain the solving space of the observation equation. The above scheme is applied to the China’s first super-resolution imaging micro-nano satellite:CX-6(02) micro-nano satellite. The imaging results of this satellite show that: our images acquisition method can obtain sufficient subpixel information, which is approximately uniformly distributed with 0.1 pixel magnitude; The result of super-resolution reconstruction is superior to the same variational bayesian method based on L1 norm prior model and total variation prior model, it is hardly to introduce or amplify the computational noise in the iterative process of the algorithm, effectively weakens the ill-posed property of the deconvolution operation. Make the CX-6(02) satellite’s imaging resolution increased from 2.8 to 1.4 meters in the 700 km orbit altitude, and the whole satellite is only 66 kg. Except for micro-nano satellite, the design scheme of this paper can also be applied to medium or large optical imaging satellite, it may provide a certain theoretical and experimental support for high resolution earth-observing remote sensing.

مفهوم

CX-6(02) micro-nano satellite;super-resolution imaging;images acquisition;reconstruction algorithm;variational Bayes;prior model

References

  1. 1.
    Babacan S D, Molina R and Katsaggelos A K. 2011. Variational Bayesian super resolution. IEEE Transactions on Image Processing, 20(4): 984–999
  2. 2.
    Chen C C. 2010. A Multi-Frame Super-Resolution Algorithm Using POCS and Wavelet. Montréal, Québec, Canada: Concordia University
  3. 3.
    Farsiu S, Robinson M D, Elad M and Milanfar P. 2004. Fast and robust multiframe super resolution. IEEE Transactions on Image Processing, 13(10): 1327–1344
  4. 4.
    He Y, Yap K H, Chen L and Chau L P. 2007. A nonlinear least square technique for simultaneous image registration and super-resolution. IEEE Transactions on Image Processing, 16(11): 2830–2841
  5. 5.
    Heinrich G and Goesele M. 2009. Variational bayes for generic topic models//Mertsching B, Hund M and Aziz Z, eds. KI 2009: Advances in Artificial Intelligence. Berlin, Heidelberg: Springer, 5803: 161–168 [DOI: 10.1007/978-3-642-04617-9_21]
  6. 6.
    Jacobsen K. 2005. High resolution imaging satellite systems//EARSeL Workshop on Remote Sensing Use of the 3rd Dimension for Remote Sensing Purposes. Porto, Portugal: [s.n.]
  7. 7.
    Latry C, Vadon H, Lefevre M J and De Boissezon H. 2003. SPOT5 THX: a 2.5m fused product//Proceedings of the 2nd GRSS/ISPRS Joint Workshop on Remote Sensing and Data Fusion over Urban Areas. Berlin, Germany, Germany: IEEE: 87–89. [DOI: 10.1109/DFUA.2003.1219963]
  8. 8.
    Murthy K, Shearn M, Smiley B D, Chau A H, Levine J and Robinson M D. 2014. SkySat-1: very high-resolution imagery from a small satellite//Proceedings Volume 9241, Sensors, Systems, and Next-generation Satellites XVIII. Amsterdam, Netherlands: SPIE, 9241: 92411E [DOI: 10.1117/12.2074163]
  9. 9.
    Nitta K, Shogenji R, Miyatake S and Tanida J. 2006. Image reconstruction for thin observation module by bound optics by using the iterative backprojection method. Applied Optics, 45(13): 2893–2900
  10. 10.
    Shen H F, Li P X and Zhang L P. 2005. A regularized super-resolution image reconstruction method. Journal of Image and Graphics, 10(4): 436–440 (
  11. 11.
    Shen H F, Zhang L P, Huang B and Li P X. 2007. A MAP approach for joint motion estimation, segmentation, and super resolution. IEEE Transactions on Image Processing, 16(2): 479–490
  12. 12.
    Sun J Y, Tan Z, Lv Q B and Pei L L. 2017. Multispectral image enhancement processing for microsat-borne imager//Proceedings of the Volume 10427, Image and Signal Processing for Remote Sensing XXIII. Warsaw, Poland: SPIE: 104271W [DOI: 10.1117/12.2280556]
  13. 13.
    Tan Z, Xiang L B, Lv Q B, Sun J Y, Zhao N, Fang Y and Liu Y Y. 2017. A sequence images super-resolution enhancement approach based on frequency-domain. Acta Optica Sinica, 37(7): 0710001 (
  14. 14.
    Villena S, Vega M, Molina R and Katsaggelos A K. 2009. Bayesian super-resolution image reconstruction using an ℓ1 prior//Proceedings of the 6th International Symposium on Image and Signal Processing and Analysis. Salzburg, Austria: IEEE: 152–157 [DOI: 10.1109/ISPA.2009.5297740]
  15. 15.
    Woods M and Katsaggelos A. 2017. A Bayesian multi-frame image super-resolution algorithm using the Gaussian Information Filter//Proceedings of 2017 IEEE International Conference on Acoustics, Speech and Signal Processing. New Orleans, LA, USA: IEEE [DOI: 10.1109/ICASSP.2017.7952380]
  16. 16.
    Zhu B, Wang X H, Tang L L and Li C R. 2010. Review on methods for snr estimation of optical remote sensing imagery. Remote Sensing Technology and Application, 25(2): 304–309 (
  17. 17.
    Zou M Y. 2001. Deconvolution and Signal Recovery. Beijing: National Defense Industry Press

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