Multiple endmember spectral unmixing within a multi-task framework

A novel spectral unmixing technique is presented which addresses the problem of spectral variability within each endmember class and determines endmember types present in each pixel. The proposed unmixing method is a multi-task framework, based on Multi-task Gaussian Process (MTGP). The Unmixing within a MTGP framework (UMTGP) is different to conventional unmixing approaches in that it assumes that spectral variation exists within each endmember class. Using synthetic and real data, the fractional abundances estimated by the UMTGP are compared with conventional methods such as Fully Constrained Least Squares (FCLS) and Multiple Endmember Spectral Mixture Analysis (MESMA). Hyperspectral data acquired from field-based platforms are used for evaluation because intra-class spectral variability is commonly large in these datasets. The results show that the UMTGP outperforms FCLS in terms of estimating fractional abundance and provides better estimates than MESMA, especially when a small number of endmember spectra for each class are available.