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An Automatic Exposure Method of Plane Array Remote Sensing Image Based on Two-Dimensional Entropy.

Tan Gao1,2,3, Liangliang Zheng1,3, Wei Xu1,3

  • 1Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China.

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|June 2, 2021
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Summary
This summary is machine-generated.

This study introduces an automatic exposure method for space cameras using 2D entropy to enhance image quality. The new approach minimizes over/underexposure, improving remote sensing image data for better on-orbit applications.

Keywords:
cubic splineexposure timeimage detailsthresholdtwo-dimensional entropy

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Area of Science:

  • Remote Sensing
  • Image Processing
  • Astrophysics

Background:

  • Improper exposure settings in space cameras lead to significant image quality degradation (overexposure/underexposure).
  • This limits the dynamic range utilization and the quality of original remote sensing images.
  • Existing automatic exposure methods may not be optimal for space-based imaging systems.

Purpose of the Study:

  • To develop an advanced automatic exposure method for plane array remote sensing images.
  • To enhance image quality by optimizing exposure time and maximizing dynamic range utilization.
  • To improve the on-orbit autonomous operating capability and application efficiency of space cameras.

Main Methods:

  • A novel image exposure quality evaluation model based on two-dimensional (2D) entropy is proposed.
  • The 2D entropy matrix is partitioned to identify saturated (over/underexposed) and unsaturated (propitious exposure) regions.
  • Cubic spline interpolation is used to fit an exposure quality curve for improved accuracy.

Main Results:

  • The proposed method significantly reduces over/underexposure compared to conventional techniques.
  • Experimental results show an increase in signal-to-noise ratio by at least 1.6730 dB.
  • The number of saturated pixels was reduced by a minimum of 2.568%.

Conclusions:

  • The 2D entropy-based automatic exposure method effectively improves space camera image quality.
  • The technique demonstrates robustness and superiority over conventional methods.
  • This advancement is crucial for enhancing autonomous operations and data utility in space missions.