Abstract
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.
Highlights
Cloud and haze are two common atmospheric phenomena that often contaminate optical remote sensing images
After reviewing and analyzing the concepts and major issues related to Haze Optimized Transformation (HOT)-based haze removal, a methodology named AutoHOT has been developed in this study to fully automate HOT process and improve the accuracy of HOT-based haze removal
AutoHOT is composed of three steps covering various aspects of a haze removal task
Summary
Cloud and haze are two common atmospheric phenomena that often contaminate optical remote sensing images. Compared to other haze removal approaches, the HOT-based method has two notable advantages: (1) it is a single scene-based method, so no haze-free reference image is required; and (2) the algorithm relies on only two visible bands, meaning that no haze-transparent band is needed, and can be applied to a broad range of remote sensing images, e.g., Landsat, MODIS, Sentinel-2, QuikBird and IKONOS. Jiang et al [23] reported a semi-automatic HOT process through searching for the clearest image windows that simultaneously meet two conditions: (1) lower radiances in visible bands; and (2) high correlation between blue and red bands This method could fail to detect the clear-sky pixels with relative higher visible radiances.
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