ERX: A Fast Real-Time Anomaly Detection Algorithm for Hyperspectral Line Scanning

Detecting unexpected objects (anomalies) in real time has great potential for monitoring, managing, and protecting the environment. Hyperspectral line-scan cameras are a low-cost solution that enhance confidence in anomaly detection over RGB and multispectral imagery. However, existing line-scan algorithms are too slow when using small computers (e.g. those onboard a drone or small satellite), do not adapt to changing scenery, or lack robustness against geometric distortions. This paper introduces the Exponentially moving RX algorithm (ERX) to address these issues, and compares it with existing RX-based anomaly detection methods for hyperspectral line scanning. Three large and more complex datasets are also introduced to better assess the practical challenges when using line-scan cameras (two hyperspectral and one multispectral). ERX is evaluated using a Jetson Xavier NX compute module, achieving the best combination of speed and detection performance. This research paves the way for future studies in grouping and locating anomalous objects, adaptive and automatic threshold selection, and real-time field tests. The datasets and the Python code are available at: https://github.com/WiseGamgee/HyperAD.
Comment: 17 pages, 13 figures, 4 tables, code and datasets accessible at https://github.com/WiseGamgee/HyperAD