  This paper addresses the problem of semi-supervised transfer learning with
limited cross-modality data in remote sensing. A large amount of multi-modal
earth observation images, such as multispectral imagery (MSI) or synthetic
aperture radar (SAR) data, are openly available on a global scale, enabling
parsing global urban scenes through remote sensing imagery. However, their
ability in identifying materials (pixel-wise classification) remains limited,
due to the noisy collection environment and poor discriminative information as
well as limited number of well-annotated training images. To this end, we
propose a novel cross-modal deep-learning framework, called X-ModalNet, with
three well-designed modules: self-adversarial module, interactive learning
module, and label propagation module, by learning to transfer more
discriminative information from a small-scale hyperspectral image (HSI) into
the classification task using a large-scale MSI or SAR data. Significantly,
X-ModalNet generalizes well, owing to propagating labels on an updatable graph
constructed by high-level features on the top of the network, yielding
semi-supervised cross-modality learning. We evaluate X-ModalNet on two
multi-modal remote sensing datasets (HSI-MSI and HSI-SAR) and achieve a
significant improvement in comparison with several state-of-the-art methods.
