ZHANG Yongjun, WANG Mengxin, WAN Yi, ZHOU Bilian, FENG Jie, CAO Bingxia, YAO Yongxiang. Compensation Model of GF-7 Panchromatic and Multispectral Image Registration Error[J]. Geomatics and Information Science of Wuhan University, 2023, 48(7): 1029-1038. DOI: 10.13203/j.whugis20220714
Citation: ZHANG Yongjun, WANG Mengxin, WAN Yi, ZHOU Bilian, FENG Jie, CAO Bingxia, YAO Yongxiang. Compensation Model of GF-7 Panchromatic and Multispectral Image Registration Error[J]. Geomatics and Information Science of Wuhan University, 2023, 48(7): 1029-1038. DOI: 10.13203/j.whugis20220714

Compensation Model of GF-7 Panchromatic and Multispectral Image Registration Error

  •   Objectives  Gaofen-7 (GF-7) is the first 1∶10 000 stereo mapping satellite of China that can produce high-resolution fusion products. The fusion products are fused with the 0.65 m-panchromatic images and 2.6 m-multispectral images. However, in some fusion products, halos and shifts are existed.
      Methods  To deal with this problem, we analyze the cross-sensor error property of push-broom optical satellite and deduce a compensation model that induces a linear term to compensate for the internal and external orientation errors, a quartic piecewise polynomial term to rectify the lens-distortion and the sub-CCD(charge coupled device) placement error, and a trigonometric series term to correct for the errors caused by platform jitter.
      Results  Several scenes of GF-7 (6 in Guangdong and 3 in Qinghai) were used to demonstrate the compensation model. The results show that the registration RMSE(root mean square error) decreased from 0.6 multispectral-pixels to about 0.07 multispectral-pixels by solving and applying the compensation model, and proportion of areas which have less-than 0.25 multispectral-pixels registration error increased to about 98%, which meets the requirement of producing high-quality fusion products.
      Conclusions  The quantitative results not only reveal the feasibility to improve the accuracy of GF-7 fusion products used by first-level data and achieve the theoretical limit accuracy, but also demonstrate the stability of the piecewise polynomial term allowing for its repeated application to further enhance compensation efficiency.
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