24 July 2000 Hyperspectral simulation of chemical weapon dispersal patterns using DIRSIG
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Fieldable thermal infrared hyperspectral imaging spectrometers has made it possible to design and construct new instruments for better detection of battlefield hazards such as chemical weapon clouds. The availability of spectroscopic measurements of these clouds can be used not only for the detection and identification of specific chemical agents but also to potentially quantify the lethality of the cloud. The simulation of chemical weapon dispersal patterns in a synthetic imaging environment offers significant benefits to sensor designers. Such an environment allows designers to easily develop trade spaces to test detection and quantification algorithms without the need for expensive and dangerous field releases. This paper discusses the implementation of a generic gas dispersion model that has been integrated into the Digital Imaging and Remote Sensing Image Generation (DIRSIG) model. The gas cloud model utilizes a 3D Gaussian distribution and first order dynamics (drift and dispersion) to drive the macro-scale cloud development and movement. The model also attempts to account for turbulence by incorporating fractional Brownian motion techniques to reproduce the micro-scale variances within the cloud. The cloud path length concentrations are then processed by the DIRSIG radiometry sub-model to compute the emission and transmission of the cloud body on a per-pixel basis. Example hyperspectral image cubes containing common agents and release amounts will be presented. Time lapse sequences will also be presented to demonstrate the evolution of the cloud over time.
© (2000) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Peter S. Arnold, Peter S. Arnold, Scott D. Brown, Scott D. Brown, John R. Schott, John R. Schott, } "Hyperspectral simulation of chemical weapon dispersal patterns using DIRSIG", Proc. SPIE 4029, Targets and Backgrounds VI: Characterization, Visualization, and the Detection Process, (24 July 2000); doi: 10.1117/12.392538; https://doi.org/10.1117/12.392538


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