While annotated images for change detection using satellite imagery are scarce and costly to obtain, there is a wealth of unlabeled images being generated every day. In order to leverage these data to learn an image representation more adequate for change detection, we explore methods that exploit the temporal consistency of Sentinel-2 times series to obtain a usable self-supervised learning signal. For this, we build and make publicly available (https://zenodo.org/record/4280482 ) the Sentinel-2 Multitemporal Cities Pairs (S2MTCP) dataset, containing multitemporal image pairs from 1520 urban areas worldwide. We test the results of multiple self-supervised learning methods for pre-training models for change detection and apply it on a public change detection dataset made of Sentinel-2 image pairs (OSCD).

Self-supervised Pre-training Enhances Change Detection in Sentinel-2 Imagery / Leenstra, M.; Marcos, D.; Bovolo, F.; Tuia, D.. - ELETTRONICO. - 12667:(2021), pp. 578-590. (Intervento presentato al convegno 25th International Conference on Pattern Recognition Workshops, ICPR 2020 tenutosi a ita nel 2021) [10.1007/978-3-030-68787-8_42].

Self-supervised Pre-training Enhances Change Detection in Sentinel-2 Imagery

Bovolo F.;
2021-01-01

Abstract

While annotated images for change detection using satellite imagery are scarce and costly to obtain, there is a wealth of unlabeled images being generated every day. In order to leverage these data to learn an image representation more adequate for change detection, we explore methods that exploit the temporal consistency of Sentinel-2 times series to obtain a usable self-supervised learning signal. For this, we build and make publicly available (https://zenodo.org/record/4280482 ) the Sentinel-2 Multitemporal Cities Pairs (S2MTCP) dataset, containing multitemporal image pairs from 1520 urban areas worldwide. We test the results of multiple self-supervised learning methods for pre-training models for change detection and apply it on a public change detection dataset made of Sentinel-2 image pairs (OSCD).
2021
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
germany
Springer Science and Business Media Deutschland GmbH
978-3-030-68786-1
978-3-030-68787-8
Leenstra, M.; Marcos, D.; Bovolo, F.; Tuia, D.
Self-supervised Pre-training Enhances Change Detection in Sentinel-2 Imagery / Leenstra, M.; Marcos, D.; Bovolo, F.; Tuia, D.. - ELETTRONICO. - 12667:(2021), pp. 578-590. (Intervento presentato al convegno 25th International Conference on Pattern Recognition Workshops, ICPR 2020 tenutosi a ita nel 2021) [10.1007/978-3-030-68787-8_42].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11572/331189
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