Self-Supervised Learning for Invariant Representations from Multi-Spectral and SAR Images. (arXiv:2205.02049v1 [cs.CV])

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 …

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