Semi-supervised learning for joint SAR and multispectral land cover classification. (arXiv:2108.09075v2 [eess.IV] UPDATED)

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 …

arxiv classification learning semi-supervised semi-supervised learning supervised learning