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Comparison of SEM and linear unmixing approaches for classification of spectral data

In recent years a number of techniques for automated classification of terrain from spectral data have been developed and applied to multispectral and hyperspectral data. Use of these techniques for hyperspectral data has presented a number of technical and practical challenges. Here we present a comparison of two fundamentally different approaches to spectral classification of data: (1) Stochastic Expectation Maximization (SEM), and (2) linear unmixing. The underlying background clutter models for each are discussed and parallels between them are explored. Parallels are drawn between estimated parameters or statistics obtained from each type of method. The mathematical parallels are then explored through application of these clutter models to airborne hyperspectral data from the NASA AVIRIS sensor. The results show surprising similarity between some of the estimates derived from these two clutter models, despite the major differences in the underlying assumptions of each.