Performance Analyzing of High Resolution Pan-Sharpening Techniques: Increasing Image Quality For Classification Using Supervised Kernel Support Vector Machine

Pan-sharpening is also known as image fusion, resolution merge, image integration, and multi sensor data fusion has been widely applied to imaging sensors. The purpose of pan-sharpening is to fuse a low spatial resolution multispectral image with a higher resolution panchromatic image to produces an image with higher spectral and spatial resolution. In this paper, we investigated these existing pan-sharpening methods based on visual and spectral analysis. And to achieve assess the accurate classification process, we proposed a support vector machine (SVM) based on radial basis function (RBF) kernel. In the Experimental results, a comparative performance analysis of techniques by various methods show that Gram-Schmidt followed by PCA perform best among all the techniques. Besides that, higher overall accuracy of Gram-Smidth (GS) fused image increase 0.90 percent. And also, the high producer’s and user’s accuracy average of Gram-Smidth (GS) fused for each of the classes and methods used was always reported greater than 91.8% and 91.11%, respectively, indicating the overall success of the performed classification. And the followed by PCA was 90.84% and 89.99.