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, real-time algorithms for these cameras must be fast when using small computers (e.g., those onboard a drone or small satellite), scalable to high dimensions, adaptable to changing scenery, and robust against geometric and radiometric distortions. This paper introduces the Exponentially moving RX algorithm (ERX) and compares it to existing RX-based anomaly detection methods for real-time line-scanning. ERX was tested using a Jetson Xavier NX compute module, achieving the best combination of speed and detection across three novel datasets compared to the other algorithms. 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 Python code for the algorithms and experiments is available at https://github.com/WiseGamgee/HyperAD.