BLIND UNMIXING USING A DOUBLE DEEP IMAGE PRIOR

In this paper, we propose a novel network structure to solve the blind hyperspectral unmixing problem using a double Deep Image Prior (DIP). In particular, the blind unmixing problem involves two sub-problems: endmember estimation and abundance estimation. We, therefore, propose two sub-networks, endmember estimation DIP (EDIP) and abundance estimation DIP (ADIP), to generate the estimation of endmembers and estimation of corresponding abundances respectively. The overall network is then constructed by assembling these two sub-networks. The network is trained in an end-to-end manner by minimizing a novel composite loss function. The experiments on synthetic and real datasets show the effectiveness of the proposed method over state-of-art unmixing methods.