  Change detection in heterogeneous multitemporal satellite images is a
challenging and still not much studied topic in remote sensing and earth
observation. This paper focuses on comparison of image pairs covering the same
geographical area and acquired by two different sensors, one optical radiometer
and one synthetic aperture radar, at two different times. We propose a
clustering-based technique to detect changes, identified as clusters that split
or merge in the different images. To evaluate potentials and limitations of our
method, we perform experiments on real data. Preliminary results confirm the
relationship between splits and merges of clusters and the occurrence of
changes. However, it becomes evident that it is necessary to incorporate prior,
ancillary, or application-specific information to improve the interpretation of
clustering results and to identify unambiguously the areas of change.
