  This work presents a novel domain adaption paradigm for studying contrastive
self-supervised representation learning and knowledge transfer using remote
sensing satellite data. Major state-of-the-art remote sensing visual domain
efforts primarily focus on fully supervised learning approaches that rely
entirely on human annotations. On the other hand, human annotations in remote
sensing satellite imagery are always subject to limited quantity due to high
costs and domain expertise, making transfer learning a viable alternative. The
proposed approach investigates the knowledge transfer of selfsupervised
representations across the distinct source and target data distributions in
depth in the remote sensing data domain. In this arrangement, self-supervised
contrastive learning-based pretraining is performed on the source dataset, and
downstream tasks are performed on the target datasets in a round-robin fashion.
Experiments are conducted on three publicly available datasets, UC Merced
Landuse (UCMD), SIRI-WHU, and MLRSNet, for different downstream classification
tasks versus label efficiency. In self-supervised knowledge transfer, the
proposed approach achieves state-of-the-art performance with label efficiency
labels and outperforms a fully supervised setting. A more in-depth qualitative
examination reveals consistent evidence for explainable representation
learning. The source code and trained models are published on GitHub.
