1. Meteorology 597A – Remote Sensing of Earth Systems
Detection of Gasoline Treated Asphalt Using Hyperspectral Imagery (HSI)
Isaac Gerg
Meteorology 597A – Remote Sensing of Earth Systems
Problem
Terrorists are embedding improvised explosive devices (IED’s) into asphalt and detonating them as vehicles pass. The IED’s are embedded into the asphalt by pouring gasoline or diesel fuel over the desired area and then igniting it. This process softens up the asphalt making it easy to remove and emplace the explosive device. Is it possible to detect this treated asphalt from untreated asphalt using hyperspectral imagery (HSI)?
Measured Spectra
Asphalt Cores
Optics
Experiment
Measure spectra of different asphalt types in nm range
Choose two target asphalt types to distinguish
Embed, at random pixel locations, several abundance amounts of target spectra into AVIRIS imagery using the 2005 AVIRIS noise model. Abundances used: [0.01:0.01: :0.1:1.0]
Unmix image to recover endmembers
Use least squares techniques to measure abundance quantification
Repeat steps three to five 1000 times
Average results
Lamp
Calibration
Results
Target 1 Detection
Target 2 Detection
Target 1 False Alarms
Target 2 False Alarms
Measurement
Target Abundances
Conclusion
Fair amount of variability between the different asphalt cores we sampled. Not much variability between the treated cores. We burned the asphalt for only seconds; realistically, it should be burned longer as we didn’t notice any softening of the material. The asphalt core was porous causing the gasoline to run through or off the core and into the pan. This prevented good heat contact with the material. However, these observations aid in generating a more realistic experimentation setting for future work.
Asphalt types are difficult to distinguish at pixel abundances less than 90%. The nonnegative least squares (NNLS) method performed the best at abundance quantification when the target was actually present in the pixel. All of the constrained least squares methods outperformed the unconstrained least squares (UCLS) method regarding false detections (false alarms).
Targets
The targets chosen for discrimination were MW 4.7 side view and M3273-SPT12. The side view of MW 4.7 was chosen since it was most exposed to the heat during the experiment.
Acknowledgments
Special thanks to Dr. Mansour Solaimanian, director of the Penn State Northeast Center of Excellence for Pavement Technology (NECEPT), for providing the asphalt cores as well as faculties to conduct the experiment.