  Multivariate measurements taken at different spatial locations occur
frequently in practice. Proper analysis of such data needs to consider not only
dependencies on-sight but also dependencies in and in-between variables as a
function of spatial separation. Spatial Blind Source Separation (SBSS) is a
recently developed unsupervised statistical tool that deals with such data by
assuming that the observable data is formed by a linear latent variable model.
In SBSS the latent variable is assumed to be constituted by weakly stationary
random fields which are uncorrelated. Such a model is appealing as further
analysis can be carried out on the marginal distributions of the latent
variables, interpretations are straightforward as the model is assumed to be
linear, and not all components of the latent field might be of interest which
acts as a form of dimension reduction. The weakly stationarity assumption of
SBSS implies that the mean of the data is constant for all sample locations,
which might be too restricting in practical applications. Therefore, an
adaptation of SBSS that uses scatter matrices based on differences was recently
suggested in the literature. In our contribution we formalize these ideas,
suggest an adapted SBSS method and show its usefulness on synthetic and real
data.
