Atmospheric correction of large-scale aerial imagery remains a major challenge, mainly due to the difficulty of accurately estimating atmospheric parameters from the images. Physical atmospheric correction methods are based on radiative transfer models, which require knowledge of atmospheric parameters at the exact time of acquisition, such as aerosol content. This study proposes a novel atmospheric correction approach based on satellite-derived Surface Reflectance (SR). The method is a semi-empirical linear correction model that leverages Pseudo-Invariant Features (PIFs) as reference points. By using the satellite-derived SR of these invariant targets as reference values, atmospheric correction of aerial images can be achieved without aerosol measurements. Experimental results show that, the proposed method achieves performance comparable to radiative transfer model approach when accurate atmospheric parameters are available, and provides more reliable corrections when such parameters are uncertain or unavailable. Also, the proposed approach reduces radiometric calibration differences between airborne and satellite images.