Atmospheric Correction of Remotely Sensed Images in Spatial and Transform Domain

Remotely sensed data is an effective source of information for monitoring changes in land useand land cover. However remotely sensed images are often degraded due to atmospheric effectsor physical limitations. Atmospheric correction minimizes or removes the atmospheric influencesthat are added to the pure signal of target and to extract more accurate information. Theatmospheric correction is often considered critical pre-processing step to achieve full spectralinformation from every pixel especially with hyperspectral and multispectral data. In this paper,multispectral atmospheric correction approaches that require no ancillary data are presented inspatial domain and transform domain. We propose atmospheric correction using linear regressionmodel based on the wavelet transform and Fourier transform. They are tested on Landsat imageconsisting of 7 multispectral bands and their performance is evaluated using visual and statisticalmeasures. The application of the atmospheric correction methods for vegetation analyses usingNormalized Difference Vegetation Index is also presented in this paper.