After preprocessing, feature extraction, matching, and other operations, the processing of remote sensing images introduces certain errors in each step, which can lead to a decrease in matching performance during subsequent control point matching. To this end, the coarse error removal algorithm is used to optimize the design of remote sensing image control point matching method. Firstly, remote sensing images are generated using sensors, and the initial remote sensing image quality is improved through steps such as filtering, defogging, and correction. Then, remote sensing image features are extracted from texture features, contour features, spectral features, and other aspects. And based on the feature extraction results, quantitatively describe the control points of remote sensing images. Finally, the matching results are obtained through two steps: coarse matching and fine matching. The coarse error removal algorithm is used to eliminate the erroneous matching point pairs in the remote sensing image control point matching results. The experimental results show that the error, redundancy, and missing matching coefficients of the optimization design method have been significantly reduced, indicating that the optimization design method has a better matching effect.