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Embedded GPU implementation of sensor correction for on-board real-time stream computing of high-resolution optical satellite imagery

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Abstract

In recent years, the data generation rate of high-resolution optical satellites is increasing rapidly, and this situation brings massive stress to the data downloading link and data processing system. To relieve downloading link stress and enhance the time efficiency of information acquisition, on-board processing needs to be introduced. Current on-board solutions are mostly based on digital signal processing and field-programmable gate array, which are obturated, lack flexibility, and are highly costly to implement. This paper takes sensor correction, the prerequisite geometric step of high-resolution optical satellite data processing, as an example, using a simulation prototype made of a double-module data parallel pipeline system based on NVIDIA embedded graphics processing unit platform, and proposes a feasible stream computing approach for low power consumption, flexible, and expandable on-board real-time processing. The experiments use a short strip of GaoFen-9 satellite data that contain 4.5 standard scenes to validate the performance and correctness of this approach. Compared to the same algorithms on the Dell PowerEdge T630 Server, the on-board version demonstrates an obvious performance advantage. When considering the contrast of power consumption for each platform, the advantage becomes even more significant. With statistics and analysis of experimental results, the timeline of processing demonstrates that this approach could meet the on-board real-time sensor correction requirement. And correctness is also verified by the root-mean-square error of pixel-by-pixel image comparison experiments.

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References

  1. Li, D., Shen, X., Gong, J., et al.: On construction of China’s space information network. Geomat. Inf. Sci. Wuhan Univ. 40(06), 711–715 (2015)

    Google Scholar 

  2. Li, D.R.: Towards geo-spatial information science in big data era. Acta Geod. Cartogr. Sin. 45(4), 379–384 (2016)

    Google Scholar 

  3. Davis, C.O., Horan, D.M., Corson, M.R.: On-orbit calibration of the Naval EarthMap Observer (NEMO) coastal ocean imaging spectrometer (COIS). Imaging Spectrom. IV 4132, 250–259 (2000)

    Google Scholar 

  4. Visser, S.J., Dawood, A.S.: Real-time natural disasters detection and monitoring from smart earth observation satellite. J. Aeros. Eng. 17(1), 10–19 (2004)

    Article  Google Scholar 

  5. Huang, J., Zhou, G.: On-board detection and matching of feature points. Remote Sens. 9(6), 601 (2017)

    Article  Google Scholar 

  6. Wu Y, Gao L, Zhang B, et al.: Embedded GPU implementation of anomaly detection for hyperspectral images. High-Performance Computing in Remote Sensing V. International Society for Optics and Photonics, vol. 9646, p. 8 (2015)

  7. Tang H., Li G., Zhang F., et al.: A spaceborne SAR on-board processing simulator using mobile GPU. In: 2016 IEEE International on Geoscience and Remote Sensing Symposium (IGARSS), pp. 1198–1201. IEEE (2016)

  8. Li C.: Parallel implementation of the recursive least square for hyperspectral image compression on GPUs. KSII Trans. Internet Inf. Syst. 11(7), 3543–3557 (2017)

    Google Scholar 

  9. Giordano, R., Guccione, P.: ROI-based on-board compression for hyperspectral remote sensing images on GPU. Sensors 17(5), 1160 (2017)

    Article  Google Scholar 

  10. Wu, Y., Li, J., Gao, L., et al.: Graphics processing unit–accelerated computation of the Markov random fields and loopy belief propagation algorithms for hyperspectral image classification. J. Appl. Remote Sens. 9(1), 7295 (2015)

    Google Scholar 

  11. Wu L., Xie X., Li W., et al.: Parallel collaborative representation for hyperspectral image classification on GPUs. In: 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 2438–2441. IEEE (2016)

  12. Li, W., Zhang, L., Zhang, L., et al.: GPU parallel implementation of isometric mapping for hyperspectral classification. IEEE Geosci. Remote Sens. Lett. 14(9), 1532–1536 (2017)

    Article  Google Scholar 

  13. Bernabé, S., Botella, G., Martín, G., et al.: Parallel implementation of a full hyperspectral unmixing chain using opencl. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 10(6), 2452–2461 (2017)

    Article  Google Scholar 

  14. Wu, Y., Gao, L., Zhang, B., et al.: Real-time implementation of optimized maximum noise fraction transform for feature extraction of hyperspectral images. J. Appl. Remote Sens. 8(1), 4797 (2014)

    Google Scholar 

  15. Hu, F.: Research on Inner FOV Stitching Theories and Algorithms for Sub-Images of Three Non-collinear TDI CCD Chips. Doctoral dissertation, Wuhan University, Wuhan, China (2010)

  16. Pan J., Zhu Y., Wang M., et al.: Parallel band-to-band registration for HJ-1A1B CCD images using openMP. In: 2011 International Symposium on Image and Data Fusion (ISIDF), pp. 1–4. IEEE (2011)

  17. Jacobsen, K.: Calibration of imaging satellite sensors. Int. Arch. Photogramm. Remote Sens. 36, 1 (2006)

    Google Scholar 

  18. Tang, X., Hu, F., Wang, M., et al.: Inner FoV stitching of spaceborne TDI CCD images based on sensor geometry and projection plane in object space. Remote Sens. 6(7), 6386–6406 (2014)

    Article  Google Scholar 

  19. Wang, M., Zhu, Y., Jin, S., et al.: Correction of ZY-3 image distortion caused by satellite jitter via virtual steady reimaging using attitude data. ISPRS J. Photogramm. Remote Sens. 119, 108–123 (2016)

    Article  Google Scholar 

  20. Wang, M., Yang, B., Hu, F., et al.: On-orbit geometric calibration model and its applications for high-resolution optical satellite imagery. Remote Sens. 6(5), 4391–4408 (2014)

    Article  Google Scholar 

  21. Xu, W., Gong, J., Wang, M.: Development, application, and prospects for Chinese land observation satellites. Geo-spat. Inf. Sci. 17(2), 102–109 (2014)

    Article  Google Scholar 

  22. Fraser, C.S., Hanley, H.B.: Bias-compensated RPCs for sensor orientation of high-resolution satellite imagery. Photogramm. Eng. Remote Sens. 71(8), 909–915 (2005)

    Article  Google Scholar 

  23. Chapman, B., Jost, G., Van Der Pas, R.: Using OpenMP: Portable Shared Memory Parallel Programming. MIT press, Cambridge (2008)

    Google Scholar 

  24. Lee, D., Dinov, I., Dong, B., et al.: CUDA optimization strategies for compute-and memory-bound neuroimaging algorithms. Comput. Methods Programs Biomed. 106(3), 175–187 (2012)

    Article  Google Scholar 

  25. Kirk, D.B., Wen-Mei, W.H.: Programming Massively Parallel Processors: A Hands-on Approach. Morgan Kaufmann, Burlington (2016)

    Google Scholar 

  26. Delgado, J., Martin, G., Plaza, J., et al.: Fast spatial preprocessing for spectral unmixing of hyperspectral data on graphics processing units. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 9(2), 952–961 (2016)

    Article  Google Scholar 

  27. NVIDIA. A Little Genius Goes A Long Way. [EB/OL], www.nvidia.com/object/embedded-systems-dev-kits-modules.html. 31 July 2017

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Acknowledgements

The authors gratefully acknowledge the associate editor and the anonymous reviewers for their outstanding comments and suggestions. This work was supported by the National Natural Science Foundation of China (Project Nos. 91438203, 91638301, 91638201, 61501036). These supports are valuable.

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Authors and Affiliations

  1. State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, 129 Luoyu Road, Wuhan, 430079, Hubei, China

    Mi Wang, Zhiqi Zhang, Ying Zhu & Zhipeng Dong

  2. Collaborative Innovation Center of Geospatial Technology, Wuhan, 430079, China

    Mi Wang

  3. School of Resource and Environmental Sciences, Wuhan University, 129 Luoyu Road, Wuhan, 430079, Hubei, China

    Ying Zhu

  4. Institute of Remote Sensing Information of Beijing, Beijing, 100192, China

    Yingying Li

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  1. Mi Wang
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  2. Zhiqi Zhang
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  3. Ying Zhu
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  4. Zhipeng Dong
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  5. Yingying Li
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Correspondence to Zhiqi Zhang.

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Wang, M., Zhang, Z., Zhu, Y. et al. Embedded GPU implementation of sensor correction for on-board real-time stream computing of high-resolution optical satellite imagery. J Real-Time Image Proc 15, 565–581 (2018). https://doi.org/10.1007/s11554-017-0741-0

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  • DOI: https://doi.org/10.1007/s11554-017-0741-0

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