Improvements in aerial images have an important role, given the increase in dust storms due to global warming and climatic changes. Apart from improving aerial images, image enhancement is important in many applications, such as tracking and monitoring studies during wars and for the environment. However, aerial images often suffer from a lack of contrast due to dust and pollutants. In this study, we propose a perceptual dark channel prior (PDCP). In this method, the light value (Y) is improved by using stretch and perceptual color space, and then the chromatic compounds (CbCr) are improved by dark channel prior (DCP). The quality measures lightness order error (LOE), entropy, and color-fullness metrics (CM) and then compares these with the improved methods (DCPMV, YCBCR, MLA, SEA, DCP, and IEIF) to determine the method efficiency. Results show that the proposed PDCP effectively enhances the aerial images better than other methods, because owning it the average are LOE (194.096), EN (7.361), and CM (39.403).