Relative radiometric calibration, or flat fielding, is indispensable for obtaining high-quality optical satellite imagery for sensors that have more than one detector per band. High-resolution optical push-broom sensors with thousands of detectors per band are now common. Multiple techniques have been employed for relative radiometric calibration. One technique, often called side-slither, where the sensor axis is rotated 90° in yaw relative to normal acquisitions, has been gaining popularity, being applied to Landsat 8, QuickBird, RapidEye, and other satellites. Side-slither can be more time efficient than some of the traditional methods, as only one acquisition may be required. In addition, the side-slither does not require any onboard calibration hardware, only a satellite capability to yaw and maintain a stable yawed attitude. A relative radiometric calibration method based on histograms of side-slither data is developed. This method has three steps: pre-processing, extraction of key points, and calculation of coefficients. Histogram matching and Otsu’s method are used to extract key points. Three datasets from the Chinese GaoFen-9 satellite were used: one to obtain the relative radiometric coefficients, and the others to verify the coefficients. Root-mean-square deviations of the corrected imagery were better than 0.1%. The maximum streaking metrics was less than 1. This method produced significantly better relative radiometric calibration than the traditional method used for GaoFen-9.

Optical focal plane assemblies are increasingly being used in high-resolution optical satellite systems to enhance the width of the image using linear push-broom imaging. With this system, vignetting occurs in the area of overlap, affecting image quality. In this paper, using the characteristics of the side-slither data, we propose side-slither data-based vignetting correction of a high-resolution spaceborne camera with an optical focal plane assembly. First, the raw side-slither data standardization is used to ensure that each row has the same features. Then, with the spatial correlation of a gray-level co-occurrence matrix, the gray-level co-occurrence matrix is proposed to identify the uniform regions, to extract the sample points. Finally, due to the characteristics of compatible linear response and non-linear response, the power-law model was used to fit, and the Levenberg–Marquardt algorithm was used to fit the model. In the experiment, polynomial fitting, laboratory coefficients and on-orbit coefficients were used for comparison with the proposed method. The side-slither data can be treated as a uniform scene due to their characteristics, and the side-slither image that was corrected using the proposed method showed less than 1% change in mean value, a root-mean-square deviation value better than 0.1%, and the average streaking metrics were superior to 0.02. The results showed that the proposed method performs significantly better in the vignetting area.