  Human civilization has an increasingly powerful influence on the earth
system. Affected by climate change and land-use change, natural disasters such
as flooding have been increasing in recent years. Earth observations are an
invaluable source for assessing and mitigating negative impacts. Detecting
changes from Earth observation data is one way to monitor the possible impact.
Effective and reliable Change Detection (CD) methods can help in identifying
the risk of disaster events at an early stage. In this work, we propose a novel
unsupervised CD method on time series Synthetic Aperture Radar~(SAR) data. Our
proposed method is a probabilistic model trained with unsupervised learning
techniques, reconstruction, and contrastive learning. The change map is
generated with the help of the distribution difference between pre-incident and
post-incident data. Our proposed CD model is evaluated on flood detection data.
We verified the efficacy of our model on 8 different flood sites, including
three recent flood events from Copernicus Emergency Management Services and six
from the Sen1Floods11 dataset. Our proposed model achieved an average of
64.53\% Intersection Over Union(IoU) value and 75.43\% F1 score. Our achieved
IoU score is approximately 6-27\% and F1 score is approximately 7-22\% better
than the compared unsupervised and supervised existing CD methods. The results
and extensive discussion presented in the study show the effectiveness of the
proposed unsupervised CD method.
