Abstract
Spatially varying haze is a common feature of most satellite images currently used for land cover classification and mapping and can significantly affect image quality. In this paper, we present a high-fidelity haze removal method based on Haze Optimized Transformation (HOT), comprising of three steps: semi-automatic HOT transform, HOT perfection and percentile based dark object subtraction (DOS). Since digital numbers (DNs) of band red and blue are highly correlated in clear sky, the R-squared criterion is utilized to search the relative clearest regions of the whole scene automatically. After HOT transform, spurious HOT responses are first masked out and filled by means of four-direction scan and dynamic interpolation, and then homomorphic filter is performed to compensate for loss of HOT of masked-out regions with large areas. To avoid patches and halo artifacts, a procedure called percentile DOS is implemented to eliminate the influence of haze. Scenes including various land cover types are selected to validate the proposed method, and a comparison analysis with HOT and Background Suppressed Haze Thickness Index (BSHTI) is performed. Three quality assessment indicators are selected to evaluate the haze removed effect on image quality from different perspective and band profiles are utilized to analyze the spectral consistency. Experiment results verify the effectiveness of the proposed method for haze removal and the superiority of it in preserving the natural color of object itself, enhancing local contrast, and maintaining structural information of original image.
Highlights
In recent years, an increasing number of projects and applications are carried out relying on moderate or high-resolution satellite images
Spurious Haze Optimized Transformation (HOT) responses are first masked out and filled by means of four-direction scan and dynamic interpolation, and homomorphic filter is performed to compensate for loss of HOT of masked-out regions with large areas
Three steps are included in our proposed method: semi-automatic HOT transform, HOT perfection and percentile dark object subtraction (DOS)
Summary
An increasing number of projects and applications are carried out relying on moderate or high-resolution satellite images. Given the constraints of satellite orbital characteristics and atmospheric conditions, comprehensive satellite data with haze-affected scenes are usually obtained. Haze removal from satellite images would normally be treated as a pre-processing step for ground information extraction [4,5]. It is feasible to remove haze from hazy images via atmospheric correction techniques, of which the desirable characteristics should involve robustness (i.e., applicable to a wide range of haze conditions), ease-to-use (i.e., minimal and simple operator) and scene-based since there typically is paucity of ancillary data [5].
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