To address problems of color cast， low sharpness， and poor performance of dark channel prior methods in processing sky regions in sand dust images， a sand dust image enhancement method based on color cast correction and sky segmentation is proposed. First， color cast in sand dust images is corrected by combining color channel compensation and the gray world algorithm. Second， a dehazing method based on sky segmentation is proposed. The image segmentation threshold is determined by information entropy， and the image is segmented into sky and non-sky regions using this threshold. A fusion window is then used to optimize the dark channel. Next， an adaptive adjustment factor is introduced to adjust the transmittance， and an atmospheric scattering model is used to restore the image， achieving the effect of removing haze. Finally， in the hue， saturation， value （HSV） space， global adaptive saturation compensation is performed on the S channel through adaptive saturation enhancement， while the V channel is enhanced with adaptive gamma correction. The proposed method improves the average gradient， standard deviation， and information entropy by 2.27%， 4.34%， and 0.25%， respectively. Experimental results show that the proposed method can correct the color cast phenomenon in sand dust images， improve image quality， and enhance the improvement effect on sky regions.