Evaluation of Aerial Real-time RX Anomaly Detection

The Reed-Xiaoli Detection (RX) algorithm is a classic algorithm commonly used to detect anomalies in hyperspectral image data, i.e. regions which are spectrally distinct from the image background. Such regions may represent interesting objects to human observers. We investigate the possibility of applying the RX algorithm to a VNIR pushbroom hyperspectral image sensor in real time onboard a small uncrewed aerial system (UAS). The generated anomaly information is much more concise and can be transmitted much faster than the raw hyperspectral data. This would enable anomalies to be automatically detected, then communicated to a ground station for immediate attention by a human observer. However, the UAS payload capacities impose strict size, weight, and power constraints. We show in what contexts the algorithm can be successfully applied and how the UAS constraints bound algorithm performance and parameters.