Contrastive Self-Supervised Data Fusion for Satellite Imagery
Journal
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Type
conference paper
Date Issued
2022
Author(s)
Research Team
AIML Lab
Abstract
Self-supervised learning has great potential for the remote sensing domain, where unlabelled observations are abundant, but labels are hard to obtain. This work leverages unlabelled multi-modal remote sensing data for augmentation-free contrastive self-supervised learning. Deep neural network models are trained to maximize the similarity of latent representations obtained with different sensing techniques from the same location, while distinguishing them from other locations. We showcase this idea with two self-supervised data fusion methods and compare against standard supervised and self-supervised learning approaches on a land-cover classification task. Our results show that contrastive data fusion is a powerful self-supervised technique to train image encoders that are capable of producing meaningful representations: Simple linear probing performs on par with fully supervised approaches and fine-tuning with as little as 10% of the labelled data results in higher accuracy than supervised training on the entire dataset.
Language
English
HSG Classification
contribution to scientific community
Publisher
ISPRS
Volume
V-3-2022
Start page
705
End page
711
Pages
6
Event Title
ISPRS Congress
Event Location
Nice
Event Date
6-11 June 2022
Subject(s)
Division(s)
Eprints ID
266528
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Name
Scheibenreif2022_ContrastiveSSLDataFusion.pdf
Size
5.92 MB
Format
Adobe PDF
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