Improving the efficiency of MESMA through geometric unmixing principles
Faculty of Sciences. Physics
Proceedings of SPIE
University of Antwerp
Spectral Mixture Analysis is a widely used image analysis tool with many applications. Yet, one of the major issues withthis technique remains the lack of ability to properly account for the spectral variability of endmembers or ground cover components that occur throughout an image scene. Endmember variability is most often addressed using iterative mixture cycles (e.g. MESMA) in which different endmember combination models are compared for each pixel. The model with the best fit is assigned to the pixel. The drawback of MESMA is the computational burden which often hampers the operational use. In an attempt to address this issue we proposed a new geometric based methodology tomore efficiently evaluate different endmember combinations in MESMA. This geometric unmixing methodology has a two-fold benefit. First of all, geometric unmixing allows a fast and fully constrained unmixing, which was previously unfeasible in MESMA due to the long processing times of the available fully constrained unmixing methods. Secondly, whereas the traditional MESMA explores all different endmember combinations separately, and selects the most appropriate combination as a final step,our approach selects the best endmember combination prior to unmixing, as suchincreasing the computational efficiency of MESMA. To do so, we built upon the equivalence between the reconstruction error in least-squares unmixing and spectral angle minimization in geometric unmixing. With the inclusion of the proposed endmember combination selection technique, the computation time decreased by a factor between 5 and 8.5, depending on the size and organization of the libraries. The spectral angle can as such be used as a proxy for model fit, enabling the selection of the proper endmember combination from large spectral libraries prior to unmixing.