IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (Jan 2024)

Evaluation and Modeling of Image Sharpness of Chinese Gaofen-1/2/6/7 Optical Remote-Sensing Satellites Over Time

  • Jiayang Cao,
  • Litao Li,
  • Yonghua Jiang,
  • Xin Shen,
  • Deren Li,
  • Meilin Tan

DOI
https://doi.org/10.1109/JSTARS.2024.3490738
Journal volume & issue
Vol. 17
pp. 20150 – 20163

Abstract

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Image sharpness assesses detail visibility in remote-sensing images and measures sensors' details resolution capability. Sensor aging and environmental changes can degrade image sharpness and quality. The Gaofen (GF) satellites provide diverse remote-sensing imagery, but evaluations of their sharpness are limited. In this study, for the GF1/2/6/7 optical remote-sensing satellites in the space-based system of the China High-Resolution Earth Observation System (CHEOS) major special project, we evaluated the relative edge response (RER), full width at half maximum (FWHM), and modulation transfer function (MTF) of the images, using nearly ten years of ground target image data. This measures image sharpness and models how it changes over time with different sensors. Within ten years of on-orbit operation, the RER and MTF (@Nyquist frequency) of GF1/2 are 0.51 and 0.50, and 0.15 and 0.11, respectively. This indicated good image edge and high-frequency detail responsiveness, with FWHM of 1.16 and 1.17, respectively, showing a slight image sharpening. For GF6, the RER, MTF (@Nyquist frequency), and FWHM were 0.42, 0.09, and 1.39, indicating improved sharpening compared with GF1/2 but decreased edge and high-frequency detail response. The RER, MTF (@Nyquist frequency), and FWHM of the panchromatic images of GF7 were 0.32, 0.04, and 1.91, which indicate image blur. Meanwhile, the corresponding indicators for the multispectral images were 0.45, 0.14, and 1.40, better than the panchromatic images. Long-term data showed periodic sharpness variations in satellite images, with GF6s stability and minimal track differences being superior. The dynamic change pattern corresponds to a fourth-order polynomial model.

Keywords