Multi-Task Learning with Low-Rank Matrix Factorization for Hyperspectral Nonlinear Unmixing

Nonlinear unmixing of hyperspectral images has been a very challenging research problem, as it needs to consider the physical interactions between the sunlight scattered by multiple materials. In this paper, we propose a new approach for nonlinear unmixing which is based on multi-task learning (MTL) with low-rank matrix factorization (LRMF). The proposed approach establishes two tasks to conduct the unmixing problem under a nonlinear mixing model. In the first task, we employ LRMF to obtain endmember signatures and their corresponding abundance fractions simultaneously. Then, the second task uses LRMF to solve interactions from multiple scattering. The effectiveness of the proposed method is verified by using real hyperspectral data. Compared with other state-of-the-art nonlinear unmixing algorithms, the proposed approach demonstrates very competitive performance.