High-precision space camera self-calibration via structural priors and intelligent optimization.

Area of Science:

• * Spacecraft engineering and remote sensing.
• * Optical system calibration and geometric parameter correction.

Background:

• * Long-term space missions degrade camera geometric parameters due to extreme environmental factors.
• * Existing calibration methods are insufficient for on-orbit correction without optical system modification.

Purpose of the Study:

• * To develop a novel on-orbit autonomous calibration method for space cameras.
• * To improve imaging quality by correcting geometric drift without altering the optical system.

Main Methods:

• * Utilizes spacecraft solar panels as an intrinsic calibration reference source.
• * Employs a line feature detection model based on space-frequency domain collaborative analysis.
• * Achieves sub-pixel level feature extraction using a dual-domain fusion strategy and an intelligent optimization algorithm.

Main Results:

• * Successfully extracted structural line features of solar panels with sub-pixel accuracy.
• * Developed and trained an optimal vanishing point model and an error function.
• * Demonstrated excellent stability and accuracy through simulations and real-world experiments.

Conclusions:

• * The proposed method offers a novel, autonomous solution for on-orbit camera calibration.
• * It effectively addresses geometric parameter drift using spacecraft structural features.
• * The technique provides a stable and accurate approach to enhance space camera imaging quality.