We propose two variational models for supervised classification of multispectral data. Both
models take into account contour and region information by minimizing a functional
compound of a data term (2D surface integral) taking into account the observation data and
knowledge on the classes, and a regularization term (1D length integral) minimizing the
length of the interfaces between regions. This is a free discontinuity problem and we have
proposed two different ways to reach such a minimum, one using a Γ-convergence …

Image classification is considered as a variational problem. In recent works, two different
models have been proposed for monospectral image classification. The goal of this paper is
to extend both models to multispectral data. The first model proposed in this paper is based
on the minimization of a criterion family whose set of solutions converges to a partition of the
data set composed of homogeneous regions with regular boundaries. The second model is
based on a set of active regions and contours. We use a level set formulation to define the …