Complete dictionary online learning for sparse unmixing

Sparse unmixing has been successfully applied to hyperspectral remote sensing imagery, based on an available standard spectral library. However, as the number of hyperspectral remote sensors increases, more and more hyperspectral remote sensing images are requiring analysis without the use of a corresponding standard spectral library. To address this problem, sparse unmixing with a complete dictionary online self-learning technique is proposed in this paper. This paper focuses on complete dictionary, which can tackle the unmixing problem with exactly atoms needed in the dataset and online learning means to process the specific data, or the current single hyperspectral remote sensing imagery, at real time. The proposed method addresses the sparse unmixing problem by considering the physical meaning of atoms in the complete dictionary, as well as a non-negative constraint for the abundance. Compared with the classical dictionary learning approaches in sparse representation theory, the experiments with two simulated hyperspectral datasets and a real dataset confirmed the effectiveness of the proposed method.