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Research process of full-polarimetric SAR calibration without using corner reflectors

  • role： First author第一作者
  • Affiliation：

    State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, 430079, China

  • Email：shi.lei@whu.edu.cn
  • Introduction：史磊，1986年生，男，副教授，研究方向为极化雷达传感器定标。E-mail： shi.lei@whu.edu.cn
SHI Lei1,  
  • Affiliation：

    State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, 430079, China

YANG Jie1,  
  • Affiliation：

    State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, 430079, China

LI Pingxiang1,  
  • Affiliation：

    State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, 430079, China

YANG Le1,  
  • Affiliation：

    School of Remote Sensing and Information Engineering, Mapping and Remote Sensing, Wuhan University, 430079, China

ZHAO Lingli2

реферат

The polarimetric synthetic aperture (PolSAR) system transmits and receives electromagnetic waves with different polarization to acquire the image measurements. Polarimetric calibration (PolCAL) is a critical stage for PolSAR image quality improvement. The general calibration technique relies on the ground deployed active and passive corner reflectors (CRs) to solve the residual system errors after the internal calibration, such as the crosstalk, channel imbalance, Faraday Rotation Angle (FRA), and so on. Although the best way to calibrate system distortion is based on ground reflectors, the manufacturing and deploying reflectors are time- and money-cost. For the common trihedral, rectangular, and pentagonal CRs, the angle bias of more than 1° between the metal plates would result in a 0.2-1 dB change of the Radar Cross Section (RCS). When the sensor wavelength increases, the ground-deployed CR length should also be enlarged to ensure that the RCS is high enough. A 1-m CR is usually required to calibrate a 0.05-m wavelength C-band satellite sensor, but a CR of more than 2 m is necessary to calibrate a 0.22-m wavelength L-band sensor. When calibrating the P-band BIOMASS, the CR length should be 5 m, which significantly increases the difficulties in the CRs manufacturing. Moreover, the azimuth and pitch angles of deployment reflector should be adjusted according the sensor pass direction and look angle. The current radar generally works on dozens of beam wave which demands heavy ground campaigns to accomplish with sensor configuration. During the operation period of the spaceborne SAR, the radiometric characteristics change with time, and the polarimetric distortions would change accordingly. Then it is of great importance to carry on the periodic calibration campaign. The regular performance of the calibration based on CRs undoubtedly increases the expenditure of time and effort. Therefore, it is vital to develop the calibration technique without using corner reflectors. This paper reports the recent research process of PolSAR calibration without using CRs. Firstly, we introduce the reason why non-reflector calibration technique is necessary and the evaluation criteria of image quality is given to help readers better understand the radar distortion. Secondly, we classify the recent non-reflector calibration methods into two categories as nature media-based calibration and corner reflector-like calibration. The properties of two categories are subsequently presented. The ways refer to the natural media, such as the in-scene vegetation in FRA-free area, to estimate the crosstalk and the cross-pol channel imbalance, while the co-pol channel imbalance may remain a constant or be solved using one or more CRs. In the scenes affected by FRA coupling with other distortions, a calibrator with no cross-polarized return may still be needed to increase the constraint for searching solutions. To get rid of the CRs, the special natural media or the CR-like point targets help to estimate the amplitude, phase of the co-pol channel imbalance and make outstanding achievements. Finally, the conclusion is given in the last section. The research on non-reflector calibration technique is meaningful and of great value for the SAR system with long wavelength.

ключеви́че слова́

radar image;polarimetric;corner reflector;calibration

References

  1. 1.
    Ainsworth T L, Ferro-Famil L and Lee J S. 2006. Orientation angle preserving a posteriori polarimetric SAR calibration. IEEE Transactions on Geoscience and Remote Sensing, 44(4): 994-1003
  2. 2.
    Chang Y L, Li P X, Yang J, Zhao J Q, Zhao L L and Shi L. 2018. Polarimetric calibration and quality assessment of the GF-3 satellite images. Sensors, 18(2): 403
  3. 3.
    Chen J, Sato M and Yang J. 2011. Polarimetric calibration using distributed odd-bounce targets//2011 IEEE International Geoscience and Remote Sensing Symposium. Vancouver: IEEE: 1079-1082
  4. 4.
    Dai Y H, Feng L, Hou X J, Choi C Y, Liu J G, Cai X B, Shi L, Zhang Y L and Gibson L. 2019. Policy-driven changes in enclosure fisheries of large lakes in the yangtze plain: evidence from satellite imagery. Science of the Total Environment, 688: 1286-1297
  5. 5.
    Ferretti A, Prati C and Rocca F. 2001. Permanent scatterers in SAR interferometry. IEEE Transactions on Geoscience and Remote Sensing, 39(1): 8-20
  6. 6.
    Freeman A. 1992. SAR calibration: an overview. IEEE Transactions on Geoscience and Remote Sensing, 30(6): 1107-1121
  7. 7.
    Garthwaite M C, Nancarrow S, Hislop A, Thankappan M, Dawson J H and Lawrie S. 2015. The design of radar corner reflectors for the australian geophysical observing system: a single design suitable for InSAR deformation monitoring and sar calibration at multiple microwave frequency bands. Record 2015/03. Geoscience Australia
  8. 8.
    Han B, Ding C B, Zhong L H, Liu J Y, Qiu X L, Hu Y X and Lei B. 2018. The GF-3 SAR data processor. Sensors, 18(3): 835
  9. 9.
    Jiang S, Qiu X L, Han B and Hu W L. 2018. A quality assessment method based on common distributed targets for GF-3 polarimetric sar data. Sensors, 18(3): 807
  10. 10.
    Kimura H. 2009. Calibration of polarimetric PALSAR imagery affected by faraday rotation using polarization orientation. IEEE Transactions on Geoscience and Remote Sensing, 47(12): 3943-3950
  11. 11.
    Lambers M and Kolb A. 2008. Automatic point target detection for interactive visual analysis of SAR images//IGARSS 2008-2008
  12. 12.
    Li Y, Hong W and Pottier E. 2015. Topography retrieval from single-pass POLSAR data based on the polarization-dependent intensity ratio. IEEE Transactions on Geoscience and Remote Sensing, 53(6): 3160-3177
  13. 13.
    Luscombe A P. 2004. Radarsat-2 SAR image quality and calibration operations. Canadian Journal of Remote Sensing, 30(3): 345-354
  14. 14.
    Morena L C, James K V and Beck J. 2004. An introduction to the RADARSAT-2 mission. Canadian Journal of Remote Sensing, 30(3): 221-234
  15. 15.
    Quegan S. 1994. A unified algorithm for phase and cross-talk calibration of polarimetric data-theory and observations. IEEE Transactions on Geoscience and Remote Sensing, 32(1): 89-99
  16. 16.
    Quegan S, Lomas M, Papathanassiou K P, Kim J S, Tebaldini S, Giudici D, Scagliola M, Guccione P, Dall J, Dubois-Fenandez P and Paillou P. 2018. Calibration challenges for the biomass P-band SAR instrument. IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium. Valencia: IEEE: 8575-8578
  17. 17.
    Quegan S and Lomas M R. 2015. The interaction between faraday rotation and system effects in synthetic aperture radar measurements of backscatter and biomass. IEEE Transactions on Geoscience and Remote Sensing, 53(8): 4299-4312
  18. 18.
    Sadeghi Z, Zoej M J V, Hooper A and Lopez-Sanchez J M. 2018. A new polarimetric persistent scatterer interferometry method using temporal coherence optimization. IEEE Transactions on Geoscience and Remote Sensing, 56(11): 6547-6555
  19. 19.
    Satake M, Matsuoka T, Umehara T, Nadai A, Uratsuka S and Fukuchi H. 2007. Polarimetric calibration experiment of ALOS PALSAR with polarization-selective dihedrals//2007 IEEE International Geoscience and Remote Sensing Symposium. Barcelona: IEEE: 1596-1598
  20. 20.
    Schneider R Z, Papathanassiou K P, Hajnsek I and Moreira A. 2006. Polarimetric and interferometric characterization of coherent scatterers in urban areas. IEEE Transactions on Geoscience and Remote Sensing, 44(4): 971-984
  21. 21.
    Schuler D L, Ainsworth T L, Lee J S and De Grandi G. 1998. Topographic mapping using polarimetric SAR data. International Journal of Remote Sensing, 19(1): 141-160
  22. 22.
    Shao Z F, Pan Y, Diao C Y and Cai J J. 2019. Cloud detection in remote sensing images based on multiscale features-convolutional neural network. IEEE Transactions on Geoscience and Remote Sensing, 57(6): 4062-4076
  23. 23.
    Shi L, Li P X, Yang J, Sun H W, Zhao L L and Zhang L P. 2020a. Polarimetric calibration for the distributed Gaofen-3 product by an improved unitary zero helix framework. ISPRS Journal of Photogrammetry and Remote Sensing, 160: 229-243
  24. 24.
    Shi L, Li P X, Yang J and Zhang L P. 2012. A statistical polarimetric decomposition solution based on the maximum-likelihood estimator. IEEE Geoscience and Remote Sensing Letters, 9(5): 861-865
  25. 25.
    Shi L, Li P X, Yang J, Zhang L P, Ding X L and Zhao L L. 2019. Co-polarization channel imbalance phase estimation by corner-reflector-like targets. ISPRS Journal of Photogrammetry and Remote Sensing, 147: 255-266
  26. 26.
    Shi L, Li P X, Yang J, Zhang L P, Ding X L and Zhao L L. 2020b. Polarimetric SAR calibration and residual error estimation when corner reflectors are unavailable. IEEE Transactions on Geoscience and Remote Sensing, 58(6): 4454-4471
  27. 27.
    Shi L, Sun W D, Yang J, Li P X and Lu L J. 2015. Building collapse assessment by the use of postearthquake Chinese VHR airborne SAR. IEEE Geoscience and Remote Sensing Letters, 12(10): 2021-2025
  28. 28.
    Shi L, Yang J and Li P X. 2014. Co-polarization channel imbalance determination by the use of bare soil. ISPRS Journal of Photogrammetry and Remote Sensing, 95: 53-67
  29. 29.
    Shi L, Zhang L F, Zhao L L, Yang J, Li P X and Zhang L P. 2013. The potential of linear discriminative laplacian eigenmaps dimensionality reduction in polarimetric SAR classification for agricultural areas. ISPRS Journal of Photogrammetry and Remote Sensing, 86: 124-135
  30. 30.
    Shimada M. 2011. Model-based polarimetric SAR calibration method using forest and surface-scattering targets. IEEE Transactions on Geoscience and Remote Sensing, 49(5): 1712-1733
  31. 31.
    Shimada M, Isoguchi O, Tadono T and Isono K. 2009. PALSAR radiometric and geometric calibration. IEEE Transactions on Geoscience and Remote Sensing, 47(12): 3915-3932
  32. 32.
    Siddique M A, Wegmüller U, Hajnsek I and Frey O. 2016. Single-look SAR tomography as an add-on to PSI for improved deformation analysis in urban areas. IEEE Transactions on Geoscience and Remote Sensing, 54(10): 6119-6137
  33. 33.
    Thompson A A, Luscombe A, James K and Fox P. 2008. RADARSAT-2 mission status: capabilities demonstrated and image quality achieved//7th European Conference on Synthetic Aperture Radar. Friedrichshafen: IEEE: 1-4
  34. 34.
    Touzi R and Shimada M. 2009. Polarimetric PALSAR calibration. IEEE Transactions on Geoscience and Remote Sensing, 47(12): 3951-3959
  35. 35.
    Ulander L M H. 2015. Trihedral corner reflector for polarimetric calibration of biomass//Proceedings of PolInSAR 2015. Frascati
  36. 36.
    Van Zyl J J. 1990. Calibration of polarimetric radar images using only image parameters and trihedral corner reflector responses. IEEE Transactions on Geoscience and Remote Sensing, 28(3): 337-348
  37. 37.
    Villa A, Iannini L, Giudici D, Monti-Guarnieri A and Tebaldini S. 2015. Calibration of SAR polarimetric images by means of a covariance matching approach. IEEE Transactions on Geoscience and Remote Sensing, 53(2): 674-686
  38. 38.
    Wang L, Yang J, Shi L, Li P X, Zhao L L and Deng S P. 2020. Impact of backscatter in Pol-InSAR forest height retrieval based on the multimodel random forest algorithm. IEEE Geoscience and Remote Sensing Letters, 17(2): 267-271
  39. 39.
    Wang Y T, Ainsworth T L and Lee J S. 2011. Assessment of system polarization quality for polarimetric sar imagery and target decomposition. IEEE Transactions on Geoscience and Remote Sensing, 49(5): 1755-1771
  40. 40.
    Zhang H, Lu W P, Zhang B, Chen J H and Wang C. 2013. Improvement of polarimetric SAR calibration based on the ainsworth algorithm for chinese airborne polsar data. IEEE Geoscience and Remote Sensing Letters, 10(4): 898-902

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