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CELESTIAL: Classification Enabled via Labelless Embeddings with Self-supervised Telescope Image Analysis Learning. (arXiv:2201.08001v1 [cs.CV])
Jan. 21, 2022, 2:10 a.m. | Suhas Kotha, Anirudh Koul, Siddha Ganju, Meher Kasam
cs.CV updates on arXiv.org arxiv.org
A common class of problems in remote sensing is scene classification, a
fundamentally important task for natural hazards identification, geographic
image retrieval, and environment monitoring. Recent developments in this field
rely label-dependent supervised learning techniques which is antithetical to
the 35 petabytes of unlabelled satellite imagery in NASA GIBS. To solve this
problem, we establish CELESTIAL-a self-supervised learning pipeline for
effectively leveraging sparsely-labeled satellite imagery. This pipeline
successfully adapts SimCLR, an algorithm that first learns image
representations on unlabelled data …
More from arxiv.org / cs.CV updates on arXiv.org
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