Anomaly Detection in Satellite Videos using Diffusion Models

TL;DR

This paper introduces a diffusion model for real-time anomaly detection in high-frequency satellite videos, effectively identifying fast-moving events like wildfires and floods without relying on motion cues.

Contribution

The work presents a novel diffusion-based approach tailored for satellite video anomaly detection, outperforming existing baseline methods in high-frequency data scenarios.

Findings

Diffusion model outperforms baseline methods in anomaly detection accuracy.

Effective in detecting fast-moving anomalies without motion components.

Applicable to real-time satellite disaster monitoring.

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.