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Synthetic image generation of chemical plumes for hyperspectral applications

机译：用于高光谱应用的化学羽流合成图像生成

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Remote sensing of factory stack plumes may provide unique information on the constituents of the plume. Potential information on the chemical composition of the factory products may be gathered from thermal emission/absorption in the infrared. We have developed a new model for generating synthetic images of plumes as viewed from a hyperspectral sensor using DIRSIG, a radiometrically based ray-tracing code. Existing plume models that describe the characteristics of the plume (constituents, concentration, and temperature) are used for input into DIRSIG. Ray-tracing is done for the scene that accounts for radiance from the plume, atmosphere and background, as well as any transmissive effects. Observations are made on the interaction between the plume and its background and possible effects for remote sensing. Images of gas plumes using a hyperspectral sensor are illustrated. Several sensitivity studies are done to demonstrate the effects of changes in plume characteristics on the resulting image. Inverse algorithms that determine the plume effluent concentration are tested on the plume images. A validation is done on the gas plume model using experimental data collected on a SF{sub}6 plume. Results show the integrated plume model to be in good agreement with the actual data from five to one hundred meters from the stack exit. The validity and limitations of these models are discussed as a result of these tests.
机译：工厂烟囱羽流的遥感可以提供关于烟囱成分的独特信息。有关工厂产品化学成分的潜在信息可以从红外线的热发射/吸收中收集。我们开发了一种新模型，用于使用 DIRSIG（一种基于辐射的光线追踪代码）生成从高光谱传感器查看的羽流合成图像。描述羽流特征（成分、浓度和温度）的现有羽流模型用于输入 DIRSIG。光线追踪是针对场景进行的，该场景考虑了羽流、大气和背景的辐射，以及任何透射效果。对羽流与其背景之间的相互作用以及对遥感的可能影响进行了观测。图示了使用高光谱传感器的气体羽流图像。进行了几项灵敏度研究，以证明羽流特征变化对所得图像的影响。在羽流图像上测试了确定羽流流出物浓度的反算法。使用在 SF{sub}6 羽流上收集的实验数据对气体羽流模型进行验证。结果表明，综合羽流模型与距烟囱出口5-100米的实际数据吻合较好。作为这些测试的结果，讨论了这些模型的有效性和局限性。

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