Dual-Path Optimization Network Based On Spectral Unmixing for Hyperspectral and Multispectral Image Fusion

In this paper, a dual-path optimized fusion network based on spectral unmixing (DPOSU) is proposed for the fusion of hyperspectral image (HSI) and multispectral image (MSI). Based on the spectral mixing model of HSI, an endmember optimization model and an abundance optimization model are constructed respectively. Combining with the observation model, a fusion model for HSI and MSI is then derived. To address the unknown spectral and spatial degradation matrices in the optimization models, a dual-path optimization network is constructed to iteratively update endmember and abundance. Comprehensive experimental results illustrate that the proposed DPOSU network outperforms several typical traditional fusion methods as well as some representative deep learning based fusion methods both visually and quantitatively.