предыдущий|следующий

A new method for high precision processing of multi-system Earth observation satellite data

  • role： First author第一作者
  • Affiliation：

    Key Laboratory of Geo-information Processing and Application System (GIPAS), Chinese Academy of Sciences, Beijing 100190, China

    Key Laboratory of Network Information System Technology (NIST), Chinese Academy of Sciences, Beijing 100190, China

    Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China

    University of Chinese Academy of Sciences, Beijing 100049, China

  • Email：fukun@aircas.ac.cn
  • Introduction：付琨，研究方向为空天遥感数据处理与应用。E-mail：fukun@aircas.ac.cn
FU Kun1234,  
  • Affiliation：

    Key Laboratory of Geo-information Processing and Application System (GIPAS), Chinese Academy of Sciences, Beijing 100190, China

    Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China

QIU Xiaolan13,  
  • Affiliation：

    Key Laboratory of Geo-information Processing and Application System (GIPAS), Chinese Academy of Sciences, Beijing 100190, China

    Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China

HAN Bing13,  
  • Affiliation：

    Key Laboratory of Network Information System Technology (NIST), Chinese Academy of Sciences, Beijing 100190, China

    Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China

SUN Xian23

реферат

Earth observation satellites, such as optical and SAR satellites, require processing such as imaging, radiometric/geometric correction, and continuous accumulation in order to provide high-precision, stable, and time continuous data and features for computer interpretation. Traditional medium and low resolution Earth observation satellites typically perform pixel-by-pixel processing based on the assumption of ideal point targets, which means that the ground object grid has a invariant time-frequency characteristic. However, the working modes of advanced satellite systems, such as high-resolution, wide-swath, large squint angle, and multi-channel, are more complex, and their data processing is very sensitive to the errors generated in the whole chain of the satellite to ground, which puts higher requirements on the accuracy of imaging parameter calibration or estimation. Hence, the method of assuming sensor pixels as ideal point targets for parameter estimation, imaging, and correction processing is no longer able to meet the processing accuracy requirements. Moreover, in recent years, the new development of multi-system satellite network collaboration and fusion applications has made it difficult to characterize and model the features of multi-source and multi-temporal data based on the current ideal point target assumption. To this end, this article proposes a new method for high-precision processing of multi-system remote sensing satellite imaging data. Firstly, the concept and characterization theory of “Hyper-pixel” are proposed, and an accurate imaging model based on hyper-pixels is established. Then, by mining stable features of hyper-pixels, and inspired by generative adversarial learning mechanisms, high-precision estimation and continuous refinement of high coupling imaging parameters are achieved. This effectively improves the accuracy of multi-system remote sensing satellite imaging data products, and provides better data input for computer interpretation.

ключеви́че слова́

remote sensing satellite;hyper-pixel;imaging processing;radiometric correction;geometric correction;deep learning

References

  1. 1.
    Cui L, Qiu X L, Guo J Y, Wen X J, Yang J Y and Fu K. 2020. A multi-channel phase error estimation method based on an error backpropagation algorithm for a multichannel SAR. Journal of Radars, 9(5): 878-885
  2. 2.
    Deng Y K, Yu W D, Zhang H, Wang W, Liu D C and Wang Y. 2020. Forthcoming spaceborne SAR development. Journal of Radars, 9(1): 1-33
  3. 3.
    Deng Y K, Zhao F J and Wang Y. 2012. Brief analysis on the development and application of spaceborne SAR. Journal of Radars, 1(1): 1-10
  4. 4.
    Freeman A. 1992. SAR calibration: an overview. IEEE Transactions on Geoscience and Remote Sensing, 30(6): 1107-1121
  5. 5.
    Fu K, Sun X, Qiu X L, Diao W H, Yan Z Y, Huang L J and Yu H F. 2021. Multi-satellite integrated processing and analysis method under remote sensing big data. Journal of Remote Sensing, 25(3): 691-707
  6. 6.
    Fu K, You H J and Hu Y F. 2007. Fine registration technology of multi-temporal spaceborne SAR image. Remote Sensing Technology and Application, 22(5): 637-641
  7. 7.
    Fu X Y, You H J and Fu K. 2012. A statistical approach to detect edges in SAR images based on square successive difference of averages. IEEE Geoscience and Remote Sensing Letters, 9(6): 1094-1098
  8. 8.
    Fu X Y, You H J and Fu K. 2012. Building segmentation from high-resolution sar images based on improved markov random field. Acta Electronica Sinica, 40(6): 1141-1147
  9. 9.
    Han B, Zhang Y J, Hu D H and Huang L J. 2011. Research on mending of space-borne sliding spotlight SAR imaging model error. Journal of Electronics and Information Technology, 33(7): 1694-1699
  10. 10.
    Jiao N G, Wang F and You H J. 2020a. A new RD-RFM stereo geolocation model for 3D geo-information reconstruction of SAR-optical satellite image pairs. IEEE Access, 8: 94654-94664
  11. 11.
    Jiao N G, Wang F, You J, Liu J Y and Qiu X L. 2020b. A generic framework for improving the geopositioning accuracy of multi-source optical and SAR imagery. ISPRS Journal of Photogrammetry and Remote Sensing, 169: 377-388
  12. 12.
    Li D R and Wang M. 2020. A review of high resolution optical satellite surveying and mapping technology. Spacecraft Recovery and Remote Sensing, 41(2): 1-11
  13. 13.
    Li D R, Wang M and Jiang J. 2021. China’s high resolution optical remote sensing satellites and their mapping applications. Geo-Spatial Information Science, 24(1): 85-94
  14. 14.
    Li J D. 2022. Advances in high-resolution earth observation satellite remote sensing technologies in China. Science and Technology Foresight, 1(1): 112-125
  15. 15.
    Li X W and Wang Y T. 2013. Prospects on future developments of quantitative remote sensing. Acta Geographica Sinica, 68(9): 1163-1169
  16. 16.
    Li Z Y, Guo J Y, Zhang Y T, Huang L J, Li J and Wu Y R. 2022. A novel autofocus algorithm for ship targets in SAR images based on the adaptive momentum estimation optimizer and space-variant minimum entropy criteria. Journal of Radars, 11(1): 83-94
  17. 17.
    Liu A P, Liu Z, Fu K and You H J. 2009. High resolution SAR images multi-layer segmentation based on graph partitioning[C]//2009 International Conference on Information Engineering and Computer Science. Wuhan: IEEE: 1-4
  18. 18.
    Luo Y T, Qiu X L, Dong Q and Fu K. 2021. A robust stereo positioning solution for multiview spaceborne SAR images based on the range–Doppler model. IEEE Geoscience and Remote Sensing Letters, 19: 4008705
  19. 19.
    Niu S R, Qiu X L, Lei B, Ding C B and Fu K. 2020. Parameter extraction based on deep neural network for SAR target simulation. IEEE Transactions on Geoscience and Remote Sensing, 58(7): 4901-4914
  20. 20.
    Niu S R, Qiu X L, Lei B and Fu K. 2021. A SAR target image simulation method with DNN embedded to calculate electromagnetic reflection. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 14: 2593-2610
  21. 21.
    Shangguan S T, Qiu X L and Fu K. 2023. Research on a special hyper-pixel for SAR radiometric monitoring. Remote Sensing, 15(8): 2175
  22. 22.
    Shangguan S T, Qiu X L, Fu K, Lei B and Hong W. 2020. GF-3 polarimetric data quality assessment based on automatic extraction of distributed targets. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13: 4282-4294
  23. 23.
    Shangguan S T, Qiu X L, Han B, Liu W J and Fu K. 2022. A novel polarimetric channel imbalance phase estimation method based on the rotated double-bounce backscatters in urban areas. Remote Sensing, 14(13): 3177
  24. 24.
    Shangguan S T, Qiu X L, Yang J T, Lei B and Din C B. 2019. Extraction and analysis of the scattering stability in urban areas based on dual-polarization SAR data. IEEE Geoscience and Remote Sensing Letters, 16(3): 427-431
  25. 25.
    Sun X, Tian Y, Lu W X, Wang P J, Niu R G, Yu H F and Fu K. 2023. From single- to multi-modal remote sensing imagery interpretation: a survey and taxonomy. Science China Information Sciences, 66(4): 140301
  26. 26.
    Sun X, Wang P J, Lu W X, Zhu Z C, Lu X N, He Q B, Li J X, Rong X E, Yang Z J, Chang H, He Q L, Yang G, Wang R P, Lu J W and Fu K. 2022. RingMo: a remote sensing foundation model with masked image modeling. IEEE Transactions on Geoscience and Remote Sensing, 61: 5612822
  27. 27.
    Wang S H, You H J and Fu K. 2012. BFSIFT: a novel method to find feature matches for SAR image registration. IEEE Geoscience and Remote Sensing Letters, 9(4): 649-653
  28. 28.
    Wu Y R. 2013. Concept on multidimensional space joint-observation SAR. Journal of Radars, 2(2): 135-142
  29. 29.
    Xiang Y M, Wang F, and You H J. 2018. OS-SIFT: a robust SIFT-like algorithm for high-resolution optical-to-SAR image registration in suburban areas. IEEE Transactions on Geoscience and Remote Sensing, 56(6): 3078-3090
  30. 30.
    Xu K F, Lei B and Zhang Y T. 2018. Retrieval of land surface emissivity using spectral and texture features based on neural network. Journal of University of Chinese Academy of Sciences, 35(1): 102-108
  31. 31.
    Xu Z Q, Zhang B C, Zhang Z, Wang M Z and Wu Y R. 2022. Nonconvex-nonlocal total variation regularization-based joint feature-enhanced sparse SAR imaging. IEEE Geoscience and Remote Sensing Letters, 19: 4515705
  32. 32.
    Yan Z Y, Wang P J, Xu F, Sun X and Diao W H. 2023. AIR-PV: a benchmark dataset for photovoltaic panels extraction in optical remote sensing imagery. Science China Information Sciences, 66(4): 140307
  33. 33.
    Yang J T, Qiu X L, Ding C B and Lei B. 2018. Identification of stable backscattering features, suitable for maintaining absolute synthetic aperture radar (SAR) radiometric calibration of sentinel-1. Remote Sensing, 10(7): 1010
  34. 34.
    Yu K, Liu S H and Zhao Y C. 2016. CPBAC: a quick atmospheric correction method using the topographic information. Remote Sensing of Environment, 186: 262-274
  35. 35.
    Zhang A D, Wu M Q, Wang D P, Cao J R and Jia W H. 2014. The Basis and Application of Remote Sensing Technology. Beijing: Science Press
  36. 36.
    Zhou G R, Xu Z Q, Fan Y Z, Zhang Z, Qiu X L, Zhang B C, Fu K and Wu Y R. 2023. HPHR-SAR-Net: Hyper high-resolution SAR imaging network based on nonlocal total variation. arXiv preprint arXiv: 2304.04428
  37. 37.
    Zhu S J, Lei B and Wu Y R. 2018. Retrieval of hyperspectral surface reflectance based on machine learning. Remote Sensing, 10(2): 323
  38. 38.
    Zhu S J, Lei B and Wu Y R. 2019. Automatic color correction for remote sensing optical image based on dense convolutional networks. Journal of University of Chinese Academy of Sciences, 36(1): 93-100

Читать полностью

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