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26 April 2007 Wide-area hyperspectral chemical plume detection using parallel random sampling
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Abstract
We present a multistage anomaly detection algorithm suite and suggest its application to chemical plume detection using hyperspectral (HS) imagery. This approach is proposed to handle underlying difficulties (e.g., plume shape/scale uncertainties) facing the development of autonomous anomaly detection algorithms. The approach features four stages: (i) scene random sampling, which does not require secondary information (shape and scale) about potential effluent plumes; (ii) anomaly detection; (iii) parallel processes, which are introduced to mitigate the inclusion by chance of potential plume samples into clutter background classes; and (iv) fusion of results. The probabilities of taking plume samples by chance within the parallel processes are modeled by the binomial distribution family, which can be used to assist on tradeoff decisions. Since this approach relies on the effectiveness of its core anomaly detection technique, we present a compact test statistic for anomaly detection, which is based on an asymmetric hypothesis test. This anomaly detection technique has shown to preserve meaningful detections (genuine anomalies in the scene) while significantly reducing the number of meaningless detections (transitions of background regions). Results of a proof of principle experiment are presented using this approach to test real HS background imagery with synthetically embedded gas plumes. Initial results are encouraging.
© (2007) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Dalton Rosario and John Romano "Wide-area hyperspectral chemical plume detection using parallel random sampling", Proc. SPIE 6554, Chemical and Biological Sensing VIII, 65540A (26 April 2007); https://doi.org/10.1117/12.720905
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