Developing a technique for the detection and removal of cloud and haze in remote sensing images

Detection and removal of cloud and haze are arduous problems in optical remote sensing imagery processing. Thick cloud and haze have the character of high reflection, so we can set the threshold to detect and remove the areas having extremely high reflection and even mosaic the images with near dates’ ones to create clear and cloudless images. Relatively, areas covered by thin cloud and haze have the spectral characteristics of both surface features and cloud and haze, thus making it difficult to separate them.
This research first processed the images with relative radiometric normalization and then transformed the images from the RGB to the HIS color model. Our assumption was that the interference of thin cloud and haze, similar to mixing a color pigment with white, would increase the color intensity and decrease the saturation of an image but would not change its hue value. Guided by this assumption, we processed the multi-temporal images and isolated areas contaminated by thin cloud and haze. The results thus suggest that an automatic method based on the HIS color model is possible for detecting thin cloud and haze on satellite images.