An Improved Hyperspectral Pansharpening Algorithm Based on Optimized Injection Model

Abstract

The hyperspectral pansharpening is a significant preprocessing technology in hyperspectral images application. A new optimized injection model-based hyperspectral pansharpening algorithm is proposed in this paper. Compared with the traditional pansharpening methods, the algorithm achieves two major improvements: 1) the total injected spatial information is obtained by integrating the spatial components of hyperspectral (HS) and panchromatic (PAN) images by PCA transformation; and 2) the gain matrix proposed in this paper is composed of two factors which constraint spectral and spatial distortions respectively. Specifically, the morphological open-closing operation and Laplacian of Gaussian enhancement scheme are used for denoising the interpolated HS and PAN images, respectively. Then, the spatial components of the denoised HS and PAN images are respectively extracted by the morphological gradient operation and homomorphic filtering. The PCA transform is applied to the results to obtain the first principal component served as total spatial details. The total spatial information weighted by the gain matrix is finally combined with the interpolated HS images to generate the pan-sharpened images, in which a new gain matrix is constructed to minimize the spectral and spatial distortions. The extensive experiments have demonstrated the potential of the proposed method in balancing spectral preservation and spatial sharpness.