Unsupervised Flood Detection on SAR Time Series

TL;DR

This paper introduces an unsupervised change detection method for flood identification using SAR time series data, leveraging probabilistic modeling, reconstruction, and contrastive learning to improve early disaster risk assessment.

Contribution

It presents a novel unsupervised change detection approach that outperforms existing methods in flood detection accuracy on multiple datasets.

Findings

Achieved an average IoU of 64.53% in flood detection.

Improved F1 score by approximately 7-22% over existing methods.

Validated on 8 flood sites, demonstrating effectiveness.

Abstract

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.