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.

翻译：利用卫星影像进行野火检测是遥感领域广泛研究的课题，在火灾边界划定与制图方面具有众多应用价值。近年来，深度学习方法已成为实现该任务自动化的可扩展解决方案，尤其是在无训练数据的无监督学习领域。这对于需要基于高分辨率卫星数据开展快速有效监测的应急风险监控尤为重要。在各种方法中，异常检测因其在计算机视觉、医学影像及遥感领域的广泛应用展现出巨大潜力。本研究基于矢量量化变分自编码器这一具有离散隐空间的经典重建型异常检测方法框架，开展无监督火烧迹地提取。我们将VQ-VAE集成到端到端框架中，并利用专用植被指数、水体指数和亮度指数进行强化后处理。基于高分辨率SPOT-6/7卫星影像的实验结果表明，该技术具有显著优势，在未来的无监督火烧迹地提取研究中展现出巨大潜力。