Blind Fusion of Hyperspectral Multispectral Images Based on Matrix Factorization

The fusion of low spatial resolution hyperspectral images and high spatial resolution multispectral images in the same scenario is important for the super-resolution of hyperspectral images. The spectral response function (SRF) and the point spread function (PSF) are two crucial prior pieces of information in fusion, and most of the current algorithms need to provide these two preliminary pieces of information in advance, even for semi-blind fusion algorithms at least the SRF. This causes limitations in the application of fusion algorithms. This paper aims to solve the dependence of the fusion method on the point spread function and proposes a method to estimate the spectral response function from the images involved in the fusion to achieve blind fusion. We conducted experiments on simulated datasets Pavia University, CAVE, and the remote sensing images acquired by two spectral cameras, Sentinel 2 and Hyperion. The experimental results show that our proposed SRF estimation method can improve the PSNR value by 5 dB on average compared with other state-of-the-art SRF estimation results. The proposed blind fusion method can improve the PSNR value of fusion results by 3-15 dB compared with other blind fusion methods.

Automated summary

The paper proposed a method to estimate the spectral response function from images for blind fusion, aiming to overcome the dependence of fusion methods on the point spread function. Experimental results showed significant improvements in the quality of fusion results using the proposed method compared to other blind fusion methods.