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Optical satellite imagery is often contaminated by the persistent presence of clouds and atmospheric haze. Without an effective method for removing this contamination, most optical remote sensing applications are less reliable. In this research, a methodology has been developed to fully automate and improve the Haze Optimized Transformation (HOT)-based haze removal. The method is referred to as AutoHOT and characterized with three notable features: a fully automated HOT process, a novel HOT image post-processing tool and a class-based HOT radiometric adjustment method. The performances of AutoHOT in haze detection and compensation were evaluated through three experiments with one Landsat-5 TM, one Landsat-7 ETM+ and eight Landsat-8 OLI scenes that encompass diverse landscapes and atmospheric haze conditions. The first experiment confirms that AutoHOT is robust and effective for haze detection. The average overall, user’s and producer’s accuracies of AutoHOT in haze detection can reach 96.4%, 97.6% and 97.5%, respectively. The second and third experiments demonstrate that AutoHOT can not only accurately characterize the haze intensities but also improve dehazed results, especially for brighter targets, compared to traditional HOT radiometric adjustment

Haze Removal Based on a Fully Automated and Improved Haze Optimized Transformation for Landsat Imagery over Land