(untitled) (Journal of Libya for geographical studies, 2025)
Remotely sensed data is an effective source of information for monitoring changes in land use and land cover. However remotely sensed images are often degraded due to atmospheric effects. Atmospheric correction minimizes removes the atmospheric influences that are added to the pure signal of target and to extract more accurate information. The atmospheric correction is often considered critical pre-processing step to achieve full spectral information from every pixel especially with hyper spectral and multispectral data. The research aims to provide multispectral atmospheric correction methods that do not require auxiliary data in the spatial domain and the transformation domain, and is proposed atmospheric correction using a linear regression model and test it on a Landsat image consisting of 7 multispectral bands and its performance is evaluated using visual and statistical measures. The application of the atmospheric correction method for vegetation analysis is also presented, which is the subtraction of dark objects. One of the most important results of this research is that the atmosphere has an effect on remotely sensed images, which causes errors in the reflectance values. Therefore, removing this effect from images using some methods, some of which are widely used to reduce fog within the image, requiring only the information in the corrected digital image data (DN) without any external information such as the dark object subtraction method, and some of them require statistical coefficients and corrected models such as the linear regression method, and both methods are sufficient to correct the satellite image from any atmospheric effect.