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• W2183619291 abstract "Hyperspectral image analysis has matured into one of the most potent and quickest growing technologies within the field of remote sensing over the past decade. Rich source of information produced in the form of spectrum at each pixel, can be used to identify surface materials. Intervening atmosphere poses an obstacle for retrieval of data, the atmospheric effects should be removed, to utilize the information for quantitative purposes. Over the years, the atmospheric correction algorithms have evolved from applied math approach to ways supported on rigorous radiative transfer modelling. They are used for the estimation of the signal below the atmosphere based on the signal quantified at the top of the atmosphere. Applied math approaches scale back atmospheric effects by empirical models that merelydepend upon statistics of image. The radiative transfer models are made at sensor radiance utilizing physics based radiative transfer equations and data from atmospheric and sun information archives. Radiative models utilize physical characteristics of the atmosphere to derive water vapour, aerosol, and mixed gas contributions to the atmospheric signal. More recently, researchers have used combinations of applied math approaches and radiativetransfer modelling approaches for the derivations of surface reflectance. This paper reviews hyperspectral atmospheric correction algorithms developed during the past years. An idealized universal atmospheric correction system has not been developed yet. Some critical elements are still lacking and need to be improved for a complete atmospheric processing." @default.
• W2183619291 created "2016-06-24" @default.
• W2183619291 creator A5038642777 @default.
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• W2183619291 date "2015-01-01" @default.
• W2183619291 modified "2023-09-23" @default.
• W2183619291 title "Atmospheric Correction Algorithms for Hyperspectral Imageries: A Review" @default.
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• W2183619291 hasPublicationYear "2015" @default.
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