  Self-Supervised learning (SSL) has become the new state-of-art in several
domain classification and segmentation tasks. Of these, one popular category in
SSL is distillation networks such as BYOL. This work proposes RSDnet, which
applies the distillation network (BYOL) in the remote sensing (RS) domain where
data is non-trivially different from natural RGB images. Since Multi-spectral
(MS) and synthetic aperture radar (SAR) sensors provide varied spectral and
spatial resolution information, we utilised them as an implicit augmentation to
learn invariant feature embeddings. In order to learn RS based invariant
features with SSL, we trained RSDnet in two ways, i.e., single channel feature
learning and three channel feature learning. This work explores the usefulness
of single channel feature learning from random MS and SAR bands compared to the
common notion of using three or more bands. In our linear evaluation, these
single channel features reached a 0.92 F1 score on the EuroSAT classification
task and 59.6 mIoU on the DFC segmentation task for certain single bands. We
also compared our results with ImageNet weights and showed that the RS based
SSL model outperforms the supervised ImageNet based model. We further explored
the usefulness of multi-modal data compared to single modality data, and it is
shown that utilising MS and SAR data learn better invariant representations
than utilising only MS data.
