  We address the problem of cross-modal information retrieval in the domain of
remote sensing. In particular, we are interested in two application scenarios:
i) cross-modal retrieval between panchromatic (PAN) and multi-spectral imagery,
and ii) multi-label image retrieval between very high resolution (VHR) images
and speech based label annotations. Notice that these multi-modal retrieval
scenarios are more challenging than the traditional uni-modal retrieval
approaches given the inherent differences in distributions between the
modalities. However, with the growing availability of multi-source remote
sensing data and the scarcity of enough semantic annotations, the task of
multi-modal retrieval has recently become extremely important. In this regard,
we propose a novel deep neural network based architecture which is considered
to learn a discriminative shared feature space for all the input modalities,
suitable for semantically coherent information retrieval. Extensive experiments
are carried out on the benchmark large-scale PAN - multi-spectral DSRSID
dataset and the multi-label UC-Merced dataset. Together with the Merced
dataset, we generate a corpus of speech signals corresponding to the labels.
Superior performance with respect to the current state-of-the-art is observed
in all the cases.
