Anomaly Detection in Satellite Videos using Diffusion Models
This addresses disaster management by enabling more effective anomaly detection in dynamic satellite data, though it appears incremental as it adapts existing diffusion models to a specific domain.
The paper tackles real-time detection of fast-moving anomalies like wildfires and floods in high-frequency satellite videos, presenting a diffusion model that outperforms baseline methods without needing motion components.
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.