  Semi-supervised learning techniques are gaining popularity due to their
capability of building models that are effective, even when scarce amounts of
labeled data are available. In this paper, we present a framework and specific
tasks for self-supervised pretraining of \textit{multichannel} models, such as
the fusion of multispectral and synthetic aperture radar images. We show that
the proposed self-supervised approach is highly effective at learning features
that correlate with the labels for land cover classification. This is enabled
by an explicit design of pretraining tasks which promotes bridging the gaps
between sensing modalities and exploiting the spectral characteristics of the
input. In a semi-supervised setting, when limited labels are available, using
the proposed self-supervised pretraining, followed by supervised finetuning for
land cover classification with SAR and multispectral data, outperforms
conventional approaches such as purely supervised learning, initialization from
training on ImageNet and other recent self-supervised approaches.
