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arXiv · 2306.05376

Anomaly Detection in Satellite Videos using Diffusion Models

Akash Awasthi·Son Ly·Jaer Nizam·Samira Zare·Videet Mehta·Safwan Ahmed·Keshav Shah·Ramakrishna Nemani·Saurabh Prasad·Hien Van Nguyen
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Abstract

The definition of anomaly detection is the identification of an unexpected event. Real-time detection of extreme events such as wildfires, cyclones, or floods using satellite data has become crucial for disaster management. Although several earth-observing satellites provide information about disasters, satellites in the geostationary orbit provide data at intervals as frequent as every minute, effectively creating a video from space. There are many techniques that have been proposed to identify anomalies in surveillance videos; however, the available datasets do not have dynamic behavior, so we discuss an anomaly framework that can work on very high-frequency datasets to find very fast-moving anomalies. In this work, we present a diffusion model which does not need any motion component to capture the fast-moving anomalies and outperforms the other baseline methods.

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Akash Awasthi, Son Ly, Jaer Nizam, Samira Zare, Videet Mehta, Safwan Ahmed, Keshav Shah, Ramakrishna Nemani, Saurabh Prasad, Hien Van Nguyen. 2023-05-25. Anomaly Detection in Satellite Videos using Diffusion Models. https://arxiv.org/abs/2306.05376

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