  Wildfire detection using satellite images is a widely studied task in remote
sensing with many applications to fire delineation and mapping. Recently, deep
learning methods have become a scalable solution to automate this task,
especially in the field of unsupervised learning where no training data is
available. This is particularly important in the context of emergency risk
monitoring where fast and effective detection is needed, generally based on
high-resolution satellite data. Among various approaches, Anomaly Detection
(AD) appears to be highly potential thanks to its broad applications in
computer vision, medical imaging, as well as remote sensing. In this work, we
build upon the framework of Vector Quantized Variational Autoencoder (VQ-VAE),
a popular reconstruction-based AD method with discrete latent spaces, to
perform unsupervised burnt area extraction. We integrate VQ-VAE into an
end-to-end framework with an intensive post-processing step using dedicated
vegetation, water and brightness indexes. Our experiments conducted on
high-resolution SPOT-6/7 images provide promising results of the proposed
technique, showing its high potential in future research on unsupervised burnt
area extraction.
