  We introduce a method for manifold alignment of different modalities (or
domains) of remote sensing images. The problem is recurrent when a set of
multitemporal, multisource, multisensor and multiangular images is available.
In these situations, images should ideally be spatially coregistred, corrected
and compensated for differences in the image domains. Such procedures require
the interaction of the user, involve tuning of many parameters and heuristics,
and are usually applied separately. Changes of sensors and acquisition
conditions translate into shifts, twists, warps and foldings of the image
distributions (or manifolds). The proposed semisupervised manifold alignment
(SS-MA) method aligns the images working directly on their manifolds, and is
thus not restricted to images of the same resolutions, either spectral or
spatial. SS-MA pulls close together samples of the same class while pushing
those of different classes apart. At the same time, it preserves the geometry
of each manifold along the transformation. The method builds a linear
invertible transformation to a latent space where all images are alike, and
reduces to solving a generalized eigenproblem of moderate size. We study the
performance of SS-MA in toy examples and in real multiangular, multitemporal,
and multisource image classification problems. The method performs well for
strong deformations and leads to accurate classification for all domains.
