Fusion of satellite images using Compressive Sampling Matching Pursuit (CoSaMP) method

Fusion of Low Resolution Multi Spectral (LRMS) image and High Resolution Panchromatic (HRPAN) image is a very important topic in the field of remote sensing. This paper handles the fusion of satellite images with sparse representation of data. The High resolution MS image is produced from the sparse, reconstructed from HRPAN and LRMS images using Compressive Sampling Matching Pursuit (CoSaMP) based on Orthogonal Matching Pursuit (OMP) algorithm. Sparse coefficients are produced by correlating the LR MS image patches with the LR PAN dictionary. The HRMS is formed by convolving the Sparse coefficients with the HR PAN dictionary. The world view -2 satellite images (HRPAN and LRMS) of Madurai, Tamil Nadu are used to test the proposed method. The experimental results show that this method can well preserve spectral and spatial details of the input images by adaptive learning. While compared to other well-known methods the proposed method offers high quality results to the input images by providing 87.28% Quality with No Reference (QNR).