  We deal with the problem of information fusion driven satellite image/scene
classification and propose a generic hallucination architecture considering
that all the available sensor information are present during training while
some of the image modalities may be absent while testing. It is well-known that
different sensors are capable of capturing complementary information for a
given geographical area and a classification module incorporating information
from all the sources are expected to produce an improved performance as
compared to considering only a subset of the modalities. However, the classical
classifier systems inherently require all the features used to train the module
to be present for the test instances as well, which may not always be possible
for typical remote sensing applications (say, disaster management). As a
remedy, we provide a robust solution in terms of a hallucination module that
can approximate the missing modalities from the available ones during the
decision-making stage. In order to ensure better knowledge transfer during
modality hallucination, we explicitly incorporate concepts of knowledge
distillation for the purpose of exploring the privileged (side) information in
our framework and subsequently introduce an intuitive modular training
approach. The proposed network is evaluated extensively on a large-scale corpus
of PAN-MS image pairs (scene recognition) as well as on a benchmark
hyperspectral image dataset (image classification) where we follow different
experimental scenarios and find that the proposed hallucination based module
indeed is capable of capturing the multi-source information, albeit the
explicit absence of some of the sensor information, and aid in improved scene
characterization.
