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 use and land cover. However remotely sensed images are often degraded due to atmospheric effects or physical limitations. Atmospheric correction minimizes or 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 hyperspectral and multispectral data. In this paper, multispectral atmospheric correction approaches that require no ancillary data are presented in spatial domain and transform domain. We propose atmospheric correction using linear regression model based on the wavelet transform and Fourier transform. They are tested on Landsat image consisting of 7 multispectral bands and their performance is evaluated using visual and statistical measures. The application of the atmospheric correction methods for vegetation analyses using Normalized Difference Vegetation Index is also presented in this paper.