Extending global-local view alignment for self-supervised learning with remote sensing imagery

arXiv:2303.06670v2 Announce Type: replace
Abstract: Since large number of high-quality remote sensing images are readily accessible, exploiting the corpus of images with less manual annotation draws increasing attention. Self-supervised models acquire general feature representations by formulating a pretext task that generates pseudo-labels for massive unlabeled data to provide supervision for training. While prior studies have explored multiple self-supervised learning techniques in remote sensing domain, pretext tasks based on local-global view alignment remain underexplored, despite achieving state-of-the-art results on natural imagery. …

alignment arxiv cs.cv global self-supervised learning sensing supervised learning type view