Hyperspectral unmixing with spectral variability using adaptive bundles and double sparsity
This addresses spectral variability issues in hyperspectral imaging for remote sensing applications, but it is incremental as it builds on existing sparsity-based methods.
The paper tackled spectral variability in hyperspectral unmixing by proposing a hierarchical mixing model with double sparsity, which adaptively recovers bundles of endmember spectra and robustly estimates abundances, showing successful determination of variable class numbers and abundances in simulated and real data.
Spectral variability is one of the major issue when conducting hyperspectral unmixing. Within a given image composed of some elementary materials (herein referred to as endmember classes), the spectral signature characterizing these classes may spatially vary due to intrinsic component fluctuations or external factors (illumination). These redundant multiple endmember spectra within each class adversely affect the performance of unmixing methods. This paper proposes a mixing model that explicitly incorporates a hierarchical structure of redundant multiple spectra representing each class. The proposed method is designed to promote sparsity on the selection of both spectra and classes within each pixel. The resulting unmixing algorithm is able to adaptively recover several bundles of endmember spectra associated with each class and robustly estimate abundances. In addition, its flexibility allows a variable number of classes to be present within each pixel of the hyperspectral image to be unmixed. The proposed method is compared with other state-of-the-art unmixing methods that incorporate sparsity using both simulated and real hyperspectral data. The results show that the proposed method can successfully determine the variable number of classes present within each class and estimate the corresponding class abundances.