Image matches are always played an important function in numerous remote sensing applications like, change discovery, cartography, combination of images taken with different sensors. In the early remote sensing systems, this task required a lot more human involvement in manually finding and selecting feature points of important landmarks. In the present day, due to the progress of local feature point detectors and descriptors, the tasks of matching and registration can be done in most of the cases automatically. Thus, besides the geometric and photometric variations, outdoor and images taken from the air that need to be matched are often decomposed and decomposed by the haze, an atmospheric phenomenon. The haze is dependent on the unknown depth information.. The current method of Dark channel priorities, for single image haze removal, dark channel prioritization is based on the stats of haze-free images. Some pixels have very lesser intensity in at least one color (RGB) channel. In the method we have proposed, process of contrast enhancement increases the perceptibility of the objects in the image like auto-contrast, histogram stretching, gamma correction, linear mapping. We begin with the global operator that improves and helps in the enhancement of the contrast of the luminance ‘L’ based on hue ‘H’ and saturation ‘S’. Our strategy is thus designed to preserve most of the local features and details in the enhanced version, the local contrast preservation is crucial in the process of matching by feature points.