Geometric Calibration of Multispectral Imaging Systems
Summary
Geometric calibration of multispectral imaging systems establishes precise spatial relationships within and between sensors operating across different spectral bands. By estimating intrinsic parameters such as focal length, principal point and lens distortion, alongside extrinsic parameters that define the rigid‐body transformation between cameras, calibration ensures that images can be accurately undistorted, registered and fused. In practice, calibration employs known control patterns or targets—often checkerboards, circle grids or coded fiducial boards—captured under varying orientations and distances. Advanced approaches integrate robust feature detection, homography decomposition and optimisation routines to refine parameter estimates and minimise reprojection error. Recent developments have increasingly focused on online or partial‐view calibration, thermal and infrared domains, and automated matching across modalities. High‐precision calibration is essential for applications ranging from planetary remote sensing and precision agriculture to structural health monitoring and industrial inspection, where metre‐ or sub‐millimetre‐level accuracy in three‐dimensional reconstruction and registration can be critical.
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Geometric Calibration of Multispectral Imaging Systems publication trend
The graph below shows the total number of articles in geometric calibration of multispectral imaging systems across all publications each year (not limited to Nature Index journals).
Technical terms
Intrinsic parameters: Internal camera parameters (focal length, principal point, distortion coefficients) defining how 3D points project onto the image plane.
Extrinsic parameters: Rigid‐body transformation (rotation and translation) relating each sensor’s coordinate frame to a common world or system frame.
Reprojection error: The pixel‐level discrepancy between observed feature points and those projected using estimated calibration parameters, used as an optimisation metric.
Calibration target: A manufactured pattern with known geometry (e.g. checkerboard, circle grid, coded fiducials) used to provide control points for calibration algorithms.
ChArUco board: A hybrid calibration target combining a chessboard pattern with ArUco fiducial markers, enabling robust and accurate feature detection even under partial views.
References
- RGB-Thermal cameras calibration based on Maximum Index Map. Computers & Electrical Engineering (2025).
- Infrared Camera Geometric Calibration: A Review and a Precise Thermal Radiation Checkerboard Target. Sensors (2023).
- A geometric calibration method for thermal cameras using a ChArUco board. Infrared Physics & Technology (2024).
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