  In the training of deep learning models, how the model parameters are
initialized greatly affects the model performance, sample efficiency, and
convergence speed. Representation learning for model initialization has
recently been actively studied in the remote sensing field. In particular, the
appearance characteristics of the imagery obtained using the a synthetic
aperture radar (SAR) sensor are quite different from those of general
electro-optical (EO) images, and thus representation learning is even more
important in remote sensing domain. Motivated from contrastive multiview
coding, we propose multi-modal representation learning for SAR semantic
segmentation. Unlike previous studies, our method jointly uses EO imagery, SAR
imagery, and a label mask. Several experiments show that our approach is
superior to the existing methods in model performance, sample efficiency, and
convergence speed.
