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. Consequently, the authors first

processed the images with relative radiometric normalization and then transformed the images from the RGB to the HIS

color model. The 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, the authors processed the multi-temporal images and isolated areas contaminated by

thin cloud and haze. The results suggest that it is possible for an automatic method based on the HIS color model to

detect thin cloud and haze on satellite images.