INVESTIGATING THE IMPACT OF PAN SHARPENING ON THE ACCURACY OF LAND COVER MAPPING IN LANDSAT OLI IMAGERY

Pan Sharpening is normally applied to sharpen a multispectral image with low resolution by using panchromatic higher resolution, generate high image. The present study aims at assessing the power of on improvement accuracy classification and land cover mapping in Landsat 8 OLI imagery. In this respect, different algorithms including Brovey, Gram-Schmidt, NNDiffuse, Principal Components were merge band (15 m) multispectral: visible infrared bands (30 m), new spatial m). Subsequently, support vector machine approach was utilized classify original resulting Sharpened images maps area. outcomes then compared through generation confusion matrix calculation kappa coefficient overall accuracy. results indicated superiority NNDiffuse algorithm imagery, an about 98.66% 0.98, respectively. Furthermore, result showed that Gram-Schmidt also slightly improved concluded useful improve depending used for purpose.