Improving Gram–Schmidt Adaptive Pansharpening Method Using Support Vector Regression and Markov Random Field

This study aimed to propose an improved Gram–Schmidt adaptive (GSA) pansharpening method using the support vector regression (SVR) and Markov random field (MRF) models in the cases of high ratios between spatial resolutions of LRMS and PAN images. In the present study, the SVR model was used to model the nonlinear relationship between the original LRMS images and the corresponding downsampled PAN image, thereby aiming to obtain the intensity component (\({\mathbf{I}}_{L}\)) of the upsampled MS image. Then, the initial pansharpened HRMS image was generated from the GSA pansharpening method with \({\mathbf{I}}_{L}\) calculated by the SVR model, which is denoted as GSA–SVR in this study. Finally, the quality of the initial pansharpened image was further improved by using the MRF model, which is denoted as GSA–SVR–MRF. A performance comparison of the GSA–SVR–MRF method with competitive pansharpening techniques as well as the GSA–SVR method demonstrated its superiority in maintaining the spatial and spectral details of the PAN and original LRMS images. The GSA–SVR–MRF method was found to be the best in terms of most quality indices.