Unsupervised Learning of Robust Representations for Change Detection on Sentinel-2 Earth Observation Images
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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.
KeywordsChange detection Unsupervised method Artificial intelligence Sentinel-2 data Vineyard use case
The work presented in this paper was supported by the H2020 CANDELA project under grant agreement No. 776193.
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