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
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.