High-precision mapping of planetary surfaces, such as Mars, relies on matched control points derived from existing georeferenced data, as ground control points (GCPs) cannot be obtained through field measurement. However, image matchers like SIFT limit the robustness of this approach, particularly on texture-scarce and self-similar Martian terrain. While deep learning-based matchers offer a new paradigm, their performance gain for bundle adjustment remains inadequately quantified. This paper systematically evaluates four matchers (hand-crafted SIFT and deep learning-based DOG+HardNet+LightGlue, DISK+LightGlue, and LoFTR), assessing their impact on georeferencing tasks using Tianwen-1 high-resolution imagery. Deep learning methods, such as LoFTR, generate more correspondence points with a more uniform spatial distribution, halving the outlier rate and improving bundle adjustment accuracy by 10%. Our study provides a benchmark for planetary mapping and shows that powerful, learning-based image matchers are pivotal for next-generation automated mapping systems.