When dealing with multi-angular image sequences, problems of reflectance
changes due either to illumination and acquisition geometry, or to
interactions with the atmosphere, naturally arise. These phenomena
interplay with the scene and lead to a modification of the measured
radiance: for example, according to the angle of acquisition, tall
objects may be seen from top or from the side and different light
scatterings may affect the surfaces. This results in shifts in the
acquired radiance, that make the problem of multi-angular classification
harder and might lead to catastrophic results, since surfaces with
the same reflectance return significantly different signals. In this
paper, rather than performing atmospheric or bi-directional reflection
distribution function (BRDF) correction, a non-linear manifold learning
approach is used to align data structures. This method maximizes
the similarity between the different acquisitions by deforming their
manifold, thus enhancing the transferability of classification models
among the images of the sequence.