Evaluation of aerial real-time RX anomaly detection

Thomas Pascarella Watson; Kevin McKenzie; Aaron Robinson; Kyle Renshaw; Ron Driggers; Eddie L. Jacobs; Joseph Conroy

doi:10.1117/12.2663904

13 June 2023 Evaluation of aerial real-time RX anomaly detection

Thomas Pascarella Watson, Kevin McKenzie, Aaron Robinson, Kyle Renshaw, Ron Driggers, Eddie L. Jacobs, Joseph Conroy

Proceedings Volume 12519, Algorithms, Technologies, and Applications for Multispectral and Hyperspectral Imaging XXIX ; 125190Q (2023) https://doi.org/10.1117/12.2663904
Event: SPIE Defense + Commercial Sensing, 2023, Orlando, Florida, United States

Abstract

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.

Conference Presentation

Citation Download Citation

Thomas Pascarella Watson, Kevin McKenzie, Aaron Robinson, Kyle Renshaw, Ron Driggers, Eddie L. Jacobs, and Joseph Conroy "Evaluation of aerial real-time RX anomaly detection", Proc. SPIE 12519, Algorithms, Technologies, and Applications for Multispectral and Hyperspectral Imaging XXIX , 125190Q (13 June 2023); https://doi.org/10.1117/12.2663904

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