The analysis of multi-modal and multi-sensor images is nowadays of
paramount importance for Earth Observation (EO) applications. There
exist a variety of methods that aim at fusing the different sources
of information to obtain a compact representation of such datasets.
However, for change detection existing methods are often unable to
deal with heterogeneous image sources and very few consider possible
nonlinearities in the data. Additionally, the availability of labeled
information is very limited in change detection applications. For
these reasons, we present the use of a semi-supervised kernel-based
feature extraction technique. It incorporates a manifold regularization
accounting for the geometric distribution and jointly addressing
the small sample problem. An exhaustive example using Landsat 5 data
illustrates the potential of the method for multi-sensor change detection.