Skip to Main content Skip to Navigation
New interface
Conference papers

Unsupervised Learning of Robust Representations for Change Detection on Sentinel-2 Earth Observation Images

Abstract : The recent popularity of artificial intelligence techniques and the wealth of free and open access Copernicus data have led to the development of new data analytics applications in the Earth Observation domain. Among them, is the detection of changes on image time series, and in particular, the estimation of levels and superficies of changes. In this paper, we propose an unsupervised framework to detect generic but relevant and reliable changes using pairs of Sentinel-2 images. To illustrate this method, we will present a scenario focusing on the detection of changes in vineyards due to natural hazards such as frost and hail.
Document type :
Conference papers
Complete list of metadata

https://hal.inria.fr/hal-03361874
Contributor : Hal Ifip Connect in order to contact the contributor
Submitted on : Friday, October 1, 2021 - 3:39:50 PM
Last modification on : Monday, April 4, 2022 - 4:16:04 PM
Long-term archiving on: : Sunday, January 2, 2022 - 7:25:21 PM

File

 Restricted access
To satisfy the distribution rights of the publisher, the document is embargoed until : 2023-01-01

Please log in to resquest access to the document

Licence


Distributed under a Creative Commons Attribution 4.0 International License

Identifiers

Citation

Michelle Aubrun, Andres Troya-Galvis, Mohanad Albughdadi, Romain Hugues, Marc Spigai. Unsupervised Learning of Robust Representations for Change Detection on Sentinel-2 Earth Observation Images. 13th International Symposium on Environmental Software Systems (ISESS), Feb 2020, Wageningen, Netherlands. pp.1-6, ⟨10.1007/978-3-030-39815-6_1⟩. ⟨hal-03361874⟩

Share

Metrics

Record views

39