On-Orbit MTF Estimation for GF-4 Satellite Using Spatial Multisampling on a New Target

Lu Han*, Kun Gao, Zeyang Dou, Zhenyu Zhu, Hong Wang, Xingke Fu

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

11 Citations (Scopus)

Abstract

GF-(Gaofen- means high resolution in Chinese) satellite launched in 2015 is the first geosynchronous orbit remote sensing satellite in China. To evaluate the on-orbit modulation transfer function (MTF) of the space-borne panchromatic camera in GF-4, a modified pulse target is proposed. This new type target is laid in the uniform low-reflection background region whose center region is a high-reflection square with the size of 3 4 ground sampled distance (GSD), and then two low-reflection rectangle stripes are extended along the center square toward both ends with the length over 4 GSD. In consideration of the staring imaging mechanism of GF-4, the target image sequences are accessed with relative random distance taken by the space-borne camera in a short period to reduce accidental error of one sampling and enhance the accuracy of estimation. Based on the multi-sampling calibrating images, maximum a posteriori estimation model and gradient descent solution with grid searching method are used to fit the pulse response function (PRF) when taking the target center positions as references. MTF is then calculated from PRF via Fourier transformation. Numerical simulation results reveal our method can keep the accuracy stable despite of different kinds of noise. Actual calibration result using this method shows that on-orbit MTF of GF-4 camera at Nyquist frequency is 0.1473.

Original languageEnglish
Article number8744329
Pages (from-to)17-21
Number of pages5
JournalIEEE Geoscience and Remote Sensing Letters
Volume17
Issue number1
DOIs
Publication statusPublished - Jan 2020

Keywords

  • GF-4 satellite
  • maximum a posteriori (MAP)
  • modified pulse target
  • modulation transfer function (MTF)
  • pulse response function (PRF)
  • spatial multisampling

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