Analytical and Bioanalytical Chemistry

, Volume 395, Issue 2, pp 337–348 | Cite as

Passive standoff detection of RDX residues on metal surfaces via infrared hyperspectral imaging

  • Thomas A. Blake
  • James F. Kelly
  • Neal B. Gallagher
  • Paul L. Gassman
  • Timothy J. Johnson
Original Paper


Hyperspectral images of galvanized steel plates, each containing a stain of cyclotrimethylenetrinitramine (RDX), were recorded using a commercial long-wave infrared imaging spectrometer. Demonstrations of passive RDX chemical detection at areal dosages between 16 and 90 µg/cm2 were carried out over practical standoff ranges between 14 and 50 m. Anomaly and target detection algorithms were applied to the images to determine the effect of areal dosage and sensing distance on detection performance for target RDX. The anomaly detection algorithms included principal component analysis, maximum autocorrelation factors, and principal autocorrelation factors. Maximum difference factors and principal difference factors are novel multivariate edge detection techniques that were examined for their utility in detection of the RDX stains in the images. A target detection algorithm based on generalized least squares was applied to the images, as well, to see if the algorithm can identify the compound in the stains on the plates using laboratory reflection spectra of RDX, cyclotetramethylenetetranitramine (HMX), and 2,4,6-trinitrotoluene (TNT) as the target spectra. The algorithm could easily distinguish between the nitroaromatic (TNT) compound and the nitramine (RDX, HMX) compounds, and, though the distinction between RDX and HMX was less clear, the mean weighted residuals identified RDX as the stain on the plate. Improvements that can be made in this detection technique are discussed in detail. As expected, it was found that detection was best for short distances and higher areal dosages. However, the target was easily detected at all distances and areal dosages used in this study.


Standoff detection Infrared hyperspectral imaging RDX HMX TNT Anomaly detection Target detection Principal component analysis Maximum autocorrelation factors Principal autocorrelation factors Generalized least squares 



The authors would like to thank Vincent Farley and Jean-Pierre Allard of Telops, Inc. for help with LW-FIRST rental, service, and training logistics.

The experimental research described here was performed at the Pacific Northwest National Laboratory (PNNL), which is operated for the US Department of Energy by the Battelle Memorial Institute under contract number AC05-76RL0 1830. This research was supported by PNNL’s Laboratory-Directed Research and Development, Initiative for Explosives Detection portfolio. The authors would like to thank the Initiative’s director Dr. David A. Atkinson for his interest and support of this work and Dr. John Hartman for his comments on the manuscript.

Supplementary material

216_2009_2907_MOESM1_ESM.pdf (1.9 mb)
ESM 1 (PDF 1906 kb)


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Copyright information

© US Government 2009

Authors and Affiliations

  • Thomas A. Blake
    • 1
  • James F. Kelly
    • 1
  • Neal B. Gallagher
    • 2
  • Paul L. Gassman
    • 1
  • Timothy J. Johnson
    • 1
  1. 1.Pacific Northwest National LaboratoryRichlandUSA
  2. 2.Eigenvector Research Inc.MansonUSA

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