  Archetypal scenarios for change detection generally consider two images
acquired through sensors of the same modality. However, in some specific cases
such as emergency situations, the only images available may be those acquired
through sensors of different modalities. This paper addresses the problem of
unsupervisedly detecting changes between two observed images acquired by
sensors of different modalities with possibly different resolutions. These
sensor dissimilarities introduce additional issues in the context of
operational change detection that are not addressed by most of the classical
methods. This paper introduces a novel framework to effectively exploit the
available information by modelling the two observed images as a sparse linear
combination of atoms belonging to a pair of coupled overcomplete dictionaries
learnt from each observed image. As they cover the same geographical location,
codes are expected to be globally similar, except for possible changes in
sparse spatial locations. Thus, the change detection task is envisioned through
a dual code estimation which enforces spatial sparsity in the difference
between the estimated codes associated with each image. This problem is
formulated as an inverse problem which is iteratively solved using an efficient
proximal alternating minimization algorithm accounting for nonsmooth and
nonconvex functions. The proposed method is applied to real images with
simulated yet realistic and real changes. A comparison with state-of-the-art
change detection methods evidences the accuracy of the proposed strategy.
