Anomaly Detection in Satellite Videos using Diffusion Models
- URL: http://arxiv.org/abs/2306.05376v1
- Date: Thu, 25 May 2023 19:17:39 GMT
- Title: Anomaly Detection in Satellite Videos using Diffusion Models
- Authors: Akash Awasthi, Son Ly, Jaer Nizam, Samira Zare, Videet Mehta, Safwan
Ahmed, Keshav Shah, Ramakrishna Nemani, Saurabh Prasad, Hien Van Nguyen
- Abstract summary: Real-time detection of extreme events using satellite data has become crucial for disaster management.
We present a diffusion model which does not need any motion component to capture the fast-moving anomalies.
- Score: 5.378437695174892
- License: http://creativecommons.org/licenses/by/4.0/
- 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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