An Informed ADMM-Based Approach for Hyperspectral Unmixing Addressing Additively-Adapted Spectral Variability

In remote sensing, hyperspectral unmixing aims to identify the pure spectral signatures, known as "endmembers", present within each pixel of a hyperspectral image and to estimate the abundance fractions of each endmember. Hyperspectral unmixing accuracy is significantly affected by the spectral variability phenomenon. In this paper, a new informed hyperspectral unmixing approach, which deals with additively-adapted spectral variability, is introduced. This approach, in addition to taking into account the spectral variability phenomenon, exploits one or more known pure material spectra present in considered hyperspectral data. Based on the alternating direction multiplier method, the proposed method decomposes the observed mixed pixel spectra into constituent spectral signatures and corresponding abundance fractions. Simulation results, using semi-real data, demonstrate the robustness of the proposed method for unmixing hyperspectral data with spectral variability.