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    This study introduces an efficient self-calibration method for remote sensing cameras (RSCs) using a micro-transceiver and deep learning. This approach eliminates the need for external ground control points, enabling independent on-orbit calibration.

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    Area of Science:

    • Photogrammetry
    • Remote Sensing
    • Machine Learning

    Background:

    • Internal parameter calibration is crucial for remote sensing cameras (RSCs).
    • On-orbit calibration typically requires external ground control points (GCPs), which are difficult to obtain for satellite platforms.
    • Existing methods face challenges with accessibility and availability of GCPs.

    Purpose of the Study:

    • To propose an efficient and independent self-calibration method for RSCs.
    • To overcome the limitations of external GCPs in on-orbit calibration.
    • To enable precise internal parameter measurement without external targets.

    Main Methods:

    • Development of a micro-transceiver for generating a supervised learning dataset.
    • Utilizing 2D diffraction grids to create auto-collimating sub-beams simulating infinite target points.
    • Application of a deep learning network to invert and determine internal camera parameters.

    Main Results:

    • The micro-transceiver generates a dataset for supervised learning.
    • The deep learning network successfully inverts learnable internal parameters.
    • The proposed method allows RSCs to calibrate independently of external ground targets.

    Conclusions:

    • The micro-transceiver and deep learning combination offers an efficient self-calibration solution for RSCs.
    • This method significantly reduces reliance on external ground control points for satellite-based cameras.
    • Independent on-orbit calibration is achievable, enhancing remote sensing photogrammetry capabilities.