Atmospheric correction of remotely sensed multispectral satellite images in transform domain

Remote sensing data provides much essential and critical information for monitoring many applications such as change detection, image fusion and land cover classification. Remotely sensed images are degraded due to the atmospheric effects. The atmospheric correction is one of the important pre processing steps to extract full spectral information from the remotely sensed images. In this paper, transform domain approaches are presented for the removal of atmospheric influences. Soft thresholding technique is adopted in wavelet transform method and gaussian high pass filter is used in homomorphic filtering. The results are tested on Landsat image consisting of 7 multispectral bands and their performance is evaluated using visual and statistical measures. The comparative analysis is done based on statistical parameters such as mean square error (MSE), peak signal to noise ratio (PSNR). Our result shows that wavelet transform is better for the removal of atmospheric effects than homomorphic filtering.