In hyperspectral image analysis the objective is to unmix a set of acquired pixels into pure spectral signatures (endmembers) and corresponding fractional abundances. The Non-negative Matrix Factorization (NMF) methods have received a lot of attention for this unmixing process. Many of these NMF based unmixing algorithms are based on sparsity regularization encouraging pure spectral endmembers, but this is not optimal for certain applications, such as foods, where abundances are not sparse. The pixels will theoretically lie on a simplex and hence the endmembers can be estimated as the vertices of the smallest enclosing simplex. In this context we present a Bayesian framework employing a volume constraint for the NMF algorithm, where the posterior distribution is numerically sampled from using a Gibbs sampling procedure. We evaluate the method on synthetical and real hyperspectral data of wheat kernels.
|Title of host publication||2009 IEEE International Workshop on Machine Learning for Signal Processing (MLSP 2009)|
|Publication status||Published - 2009|
|Event||2009 IEEE International Workshop on Machine Learning for Signal Processing - Grenoble, France|
Duration: 2 Sep 2009 → 4 Sep 2009
|Workshop||2009 IEEE International Workshop on Machine Learning for Signal Processing|
|Period||02/09/2009 → 04/09/2009|