Hyperspectral imaging data atmospheric correction challenges and solutions using QUAC and FLAASH algorithms

Recently, Hyperspectral remote sensing technology has been proved to be a valuable tool to get reliable information with details for identifying different objects on the earth surface with high spectral resolution. Due to atmospheric effects the valuable information may be lost from hyperspectral data. Hence it is necessary to remove these effects from hyperspectral data for reliable identification of the objects on the earth surface. The atmospheric correction is a very critical task of hyperspectral images. The present paper highlights the advantages of hyperspectral data, challenges over it as a pre-processing with solutions through QUAC and FLAASH algorithms. The hyperspectral data acquired for Aurangabad district were used to test these algorithms. The result indicates that the size of hyperspectral image can be reduced. The ENVI 5.1 software with IDL language is an efficient way to visualize and analysis the hyperspectral images. Implementation of atmospheric correction algorithms like QUAC and FLAASH is successfully carried out. The QUAC model gives accurate and reliable results without any ancillary information but requires only wavelength and radiometric calibration with less time than FLAASH.