Unsupervised flood detection on SAR time series using variational autoencoder

Unsupervised flood detection on SAR time series using variational autoencoder

In this study, we propose a novel unsupervised Change Detection (CD) model to detect flood extent using Synthetic Aperture Radar (SAR) time series data. The proposed model is based on a spatiotemporal variational autoencoder, trained with reconstruction and contrastive learning techniques. The chang...

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Bibliographic Details
Main Authors: Ritu Yadav, Andrea Nascetti, Hossein Azizpour, Yifang Ban
Format: Article
Language:English
Published: Elsevier 2024-02-01
Series:International Journal of Applied Earth Observations and Geoinformation
Subjects:
SAR
Time series
Contrastive learning
VAE
Unsupervised change detection
Flood detection
Online Access:http://www.sciencedirect.com/science/article/pii/S1569843223004594

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http://www.sciencedirect.com/science/article/pii/S1569843223004594

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