Comparison of Several Hyperspectral Image Fusion Methods for Superresolution

Hyperspectral image applications have been explored in various areas, but they are often suffered from coarser spatial resolutions. In recent years, many hyperspectral image fusion approaches which merge hyperspectral image with multi-spectral or panchromatic one have been presented to improve the spatial resolution of hyperspectral image. In this paper, we compared four state-of-the-art hyperspectral fusion methods, namely coupled nonnegative matrix factorization (CNMF) method, sparse matrix factorization (SPMF) method, hyperspectral Image superresolution (HySure) method and sparse representation (SPRE) method. The main idea of each method is depicted briefly, five statistical assessment parameters, namely cross correlation (CC), root-mean-square error (RMSE), spectral angle mapper (SAM), universal image quality index (UIQI), and relative dimensionless global error in synthesis (ERGAS) are adopted to comparatively analyze the fusion results. The experimental results show that the effect of method based on sparse representation is superior to the others one.